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Sample records for single spike trains

  1. Stochastic models for spike trains of single neurons

    CERN Document Server

    Sampath, G

    1977-01-01

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

  2. Stochastic optimal control of single neuron spike trains

    DEFF Research Database (Denmark)

    Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë

    2014-01-01

    stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy...... into account physiological constraints on the control. A precise and robust targeting of neural activity based on stochastic optimal control has great potential for regulating neural activity in e.g. prosthetic applications and to improve our understanding of the basic mechanisms by which neuronal firing...

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

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    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

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

    Directory of Open Access Journals (Sweden)

    Arno Onken

    2016-11-01

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

  5. Spike Train SIMilarity Space (SSIMS): a frame-work for single neuron and ensemble data analysis

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    Vargas-Irwin, Carlos E.; Brandman, David M.; Zimmermann, Jonas B.; Donoghue, John P.; Black, Michael J.

    2014-01-01

    Increased emphasis on circuit level activity in the brain makes it necessary to have methods to visualize and evaluate large scale ensemble activity, beyond that revealed by raster-histograms or pairwise correlations. We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how Spike train SIMilarity Space (SSIMS) analysis captures the relationship between goal directions for an 8-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models. PMID:25380335

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

    Science.gov (United States)

    Shrestha, Sumit Bam; Song, Qing

    2017-12-01

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

  7. Inferring oscillatory modulation in neural spike trains.

    Science.gov (United States)

    Arai, Kensuke; Kass, Robert E

    2017-10-01

    Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak.

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

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

    Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

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

    Science.gov (United States)

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

    2014-02-05

    The action potential activity of single cortical neurons can evoke measurable sensory effects, but it is not known how spiking parameters and neuronal subtypes affect the evoked sensations. Here, we examined the effects of spike train irregularity, spike frequency, and spike number on the detectability of single-neuron stimulation in rat somatosensory cortex. For regular-spiking, putative excitatory neurons, detectability increased with spike train irregularity and decreasing spike frequencies but was not affected by spike number. Stimulation of single, fast-spiking, putative inhibitory neurons led to a larger sensory effect compared to regular-spiking neurons, and the effect size depended only on spike irregularity. An ideal-observer analysis suggests that, under our experimental conditions, rats were using integration windows of a few hundred milliseconds or more. Our data imply that the behaving animal is sensitive to single neurons' spikes and even to their temporal patterning. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. Training spiking neural networks to associate spatio-temporal input-output spike patterns

    OpenAIRE

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

    2013-01-01

    In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–output spike trains) [1] we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotemporal spike patterns to desired output spike patterns. The algorithm is based on the conversion of spike trains into analogue signals and the application of the Widrow–Hoff learning rule. In this paper we present a mathematical formulation of the prop...

  11. Towards statistical summaries of spike train data.

    Science.gov (United States)

    Wu, Wei; Srivastava, Anuj

    2011-01-30

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

  12. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    1996-11-01

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

  14. Modeling Spike-Train Processing in the Cerebellum Granular Layer and Changes in Plasticity Reveal Single Neuron Effects in Neural Ensembles

    Directory of Open Access Journals (Sweden)

    Chaitanya Medini

    2012-01-01

    Full Text Available The cerebellum input stage has been known to perform combinatorial operations on input signals. In this paper, two types of mathematical models were used to reproduce the role of feed-forward inhibition and computation in the granular layer microcircuitry to investigate spike train processing. A simple spiking model and a biophysically-detailed model of the network were used to study signal recoding in the granular layer and to test observations like center-surround organization and time-window hypothesis in addition to effects of induced plasticity. Simulations suggest that simple neuron models may be used to abstract timing phenomenon in large networks, however detailed models were needed to reconstruct population coding via evoked local field potentials (LFP and for simulating changes in synaptic plasticity. Our results also indicated that spatio-temporal code of the granular network is mainly controlled by the feed-forward inhibition from the Golgi cell synapses. Spike amplitude and total number of spikes were modulated by LTP and LTD. Reconstructing granular layer evoked-LFP suggests that granular layer propagates the nonlinearities of individual neurons. Simulations indicate that granular layer network operates a robust population code for a wide range of intervals, controlled by the Golgi cell inhibition and is regulated by the post-synaptic excitability.

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

    Science.gov (United States)

    Koyama, Shinsuke

    2015-07-01

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

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

    Science.gov (United States)

    Tezuka, Taro

    2018-06-01

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

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

    Science.gov (United States)

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

    2010-11-01

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

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

    Science.gov (United States)

    Mulansky, Mario; Bozanic, Nebojsa

    2015-01-01

    Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. PMID:25744888

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

    Science.gov (United States)

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

    2013-05-01

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

  20. The Omega-Infinity Limit of Single Spikes

    CERN Document Server

    Axenides, Minos; Linardopoulos, Georgios

    A new infinite-size limit of strings in RxS2 is presented. The limit is obtained from single spike strings by letting their angular velocity omega become infinite. We derive the energy-momenta relation of omega-infinity single spikes as their linear velocity v-->1 and their angular momentum J-->1. Generally, the v-->1, J-->1 limit of single spikes is singular and has to be excluded from the spectrum and be studied separately. We discover that the dispersion relation of omega-infinity single spikes contains logarithms in the limit J-->1. This result is somewhat surprising, since the logarithmic behavior in the string spectra is typically associated with their motion in non-compact spaces such as AdS. Omega-infinity single spikes seem to completely cover the surface of the 2-sphere they occupy, so that they may essentially be viewed as some sort of "brany strings". A proof of the sphere-filling property of omega-infinity single spikes is given in the appendix.

  1. Nonlinear evolution of single spike in Richtmyer-Meshkov instability

    International Nuclear Information System (INIS)

    Fukuda, Y.; Nishihara, K.; Wouchuk, J.G.

    2000-01-01

    Nonlinear evolution of single spike structure and vortex in the Richtmyer-Meshkov instability is investigated with the use of a two-dimensional hydrodynamic code. It is shown that singularity appears in the vorticity left by transmitted and reflected shocks at a corrugated interface. This singularity results in opposite sign of vorticity along the interface that causes double spiral structure of the spike. (authors)

  2. Joint Probability-Based Neuronal Spike Train Classification

    Directory of Open Access Journals (Sweden)

    Yan Chen

    2009-01-01

    Full Text Available Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs. The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.

  3. Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method.

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    Xie, Xiurui; Qu, Hong; Yi, Zhang; Kurths, Jurgen

    2017-06-01

    The spiking neural network (SNN) is the third generation of neural networks and performs remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode mechanism found in biological hippocampus enables SNN to possess more powerful computation capability than networks with other encoding schemes. However, this temporal encoding approach requires neurons to process information serially on time, which reduces learning efficiency significantly. To keep the powerful computation capability of the temporal encoding mechanism and to overcome its low efficiency in the training of SNNs, a new training algorithm, the accurate synaptic-efficiency adjustment method is proposed in this paper. Inspired by the selective attention mechanism of the primate visual system, our algorithm selects only the target spike time as attention areas, and ignores voltage states of the untarget ones, resulting in a significant reduction of training time. Besides, our algorithm employs a cost function based on the voltage difference between the potential of the output neuron and the firing threshold of the SNN, instead of the traditional precise firing time distance. A normalized spike-timing-dependent-plasticity learning window is applied to assigning this error to different synapses for instructing their training. Comprehensive simulations are conducted to investigate the learning properties of our algorithm, with input neurons emitting both single spike and multiple spikes. Simulation results indicate that our algorithm possesses higher learning performance than the existing other methods and achieves the state-of-the-art efficiency in the training of SNN.

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

    Science.gov (United States)

    Aronov, Dmitriy; Victor, Jonathan D

    2004-06-01

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

  5. Impact of substance P on the correlation of spike train evoked by electro acupuncture

    International Nuclear Information System (INIS)

    Jin, Chen; Zhang, Xuan; Wang, Jiang; Guo, Yi; Zhao, Xue; Guo, Yong-Ming

    2016-01-01

    Highlights: • We analyze spike trains induced by EA before and after inhibiting SP in PC6 area. • Inhibiting SP leads to an increase of spiking rate of median nerve. • SP may modulate membrane potential to affect the spiking rate. • SP has an influence on long-range correlation of spike train evoked by EA. • SP play an important role in EA-induced neural spiking and encoding. - Abstract: Substance P (SP) participates in the neural signal transmission evoked by electro-acupuncture (EA). This paper investigates the impact of SP on the correlation of spike train in the median nerve evoked by EA at 'Neiguan' acupoint (PC6). It shows that the spiking rate and interspike interval (ISI) distribution change obviously after inhibiting SP. This variation of spiking activity indicates that SP affects the temporal structure of spike train through modulating the action potential on median nerve filaments. Furtherly, the correlation coefficient and scaling exponent are considered to measure the correlation of spike train. Scaled Windowed Variance (SWV) method is applied to calculate scaling exponent which quantifies the long-range correlation of the neural electrical signals. It is found that the correlation coefficients of ISI increase after inhibiting SP released. In addition, the scaling exponents of neuronal spike train have significant differences between before and after inhibiting SP. These findings demonstrate that SP has an influence on the long-range correlation of spike train. Our results indicate that SP may play an important role in EA-induced neural spiking and encoding.

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

    Science.gov (United States)

    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.

  7. An STDP training algorithm for a spiking neural network with dynamic threshold neurons.

    Science.gov (United States)

    Strain, T J; McDaid, L J; McGinnity, T M; Maguire, L P; Sayers, H M

    2010-12-01

    This paper proposes a supervised training algorithm for Spiking Neural Networks (SNNs) which modifies the Spike Timing Dependent Plasticity (STDP)learning rule to support both local and network level training with multiple synaptic connections and axonal delays. The training algorithm applies the rule to two and three layer SNNs, and is benchmarked using the Iris and Wisconsin Breast Cancer (WBC) data sets. The effectiveness of hidden layer dynamic threshold neurons is also investigated and results are presented.

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

    Science.gov (United States)

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

    2010-12-15

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

  9. Detection of hidden structures in nonstationary spike trains.

    Science.gov (United States)

    Takiyama, Ken; Okada, Masato

    2011-05-01

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

  10. Stochastic resonance of ensemble neurons for transient spike trains: Wavelet analysis

    International Nuclear Information System (INIS)

    Hasegawa, Hideo

    2002-01-01

    By using the wavelet transformation (WT), I have analyzed the response of an ensemble of N (=1, 10, 100, and 500) Hodgkin-Huxley neurons to transient M-pulse spike trains (M=1 to 3) with independent Gaussian noises. The cross correlation between the input and output signals is expressed in terms of the WT expansion coefficients. The signal-to-noise ratio (SNR) is evaluated by using the denoising method within the WT, by which the noise contribution is extracted from the output signals. Although the response of a single (N=1) neuron to subthreshold transient signals with noises is quite unreliable, the transmission fidelity assessed by the cross correlation and SNR is shown to be much improved by increasing the value of N: a population of neurons plays an indispensable role in the stochastic resonance (SR) for transient spike inputs. It is also shown that in a large-scale ensemble, the transmission fidelity for suprathreshold transient spikes is not significantly degraded by a weak noise which is responsible to SR for subthreshold inputs

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

    Directory of Open Access Journals (Sweden)

    Nagarajan eVenkateswaran

    2012-02-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Czech Academy of Sciences Publication Activity Database

    Šíma, Jiří; Sgall, Jiří

    2005-01-01

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

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

    Science.gov (United States)

    Satuvuori, Eero; Kreuz, Thomas

    2018-04-01

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

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Varsha H. Rallapalli

    2016-10-01

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

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

    DEFF Research Database (Denmark)

    Sacerdote, Laura; Tamborrino, Massimiliano; Zucca, Cristina

    2011-01-01

    recorded spike trains. We develop a non-parametric method based on copulas, that we apply to simulated data according to different bivariate Leaky In- tegrate and Fire models. The method discerns dependencies determined by the surround- ing network, from those determined by direct interactions between......The dynamics of a neuron are influenced by the connections with the network where it lies. Recorded spike trains exhibit patterns due to the interactions between neurons. However, the structure of the network is not known. A challenging task is to investigate it from the analysis of simultaneously...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  20. Non-invasive single-trial detection of variable population spike responses in human somatosensory evoked potentials.

    Science.gov (United States)

    Waterstraat, Gunnar; Scheuermann, Manuel; Curio, Gabriel

    2016-03-01

    Somatosensory evoked potentials (SEPs) around 600 Hz ('σ-bursts') are correlates of cortical population spikes. Recently, single-trial σ-bursts were detected in human scalp EEG using 29-channel low-noise recordings in an electromagnetically shielded room. To achieve clinical applicability, this study aimed to establish a protocol using only 8 EEG channels in an unshielded environment and to quantify the variability of σ-bursts. Median nerve SEPs were recorded in 10 healthy subjects using a custom-built low-noise EEG amplifier. A detection algorithm for single-trial σ-bursts was trained as combination of spatio-temporal filters and a non-linear classifier. The single-trial responses were probed for the presence of significant increases of amplitude and variability. Single-trial σ-burst detection succeeded with Detection Rates and Positive Predictive Values above 80% in subjects with high SNR. A significant inter-trial variability in the amplitudes of early low-frequency SEPs and σ-bursts could be demonstrated. Single-trial σ-bursts can be detected on scalp-EEG using only 8 EEG channels in an electromagnetically disturbed environment. The combination of dedicated hardware and detection algorithms allows quantifying and describing their variability. The variability of population spikes in the human somatosensory cortex can be traced non-invasively in a clinical setting. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  1. Nonlinear evolution of single spike structure and vortex in Richtmeyer-Meshkov instability

    International Nuclear Information System (INIS)

    Fukuda, Yuko O.; Nishihara, Katsunobu; Okamoto, Masayo; Nagatomo, Hideo; Matsuoka, Chihiro; Ishizaki, Ryuichi; Sakagami, Hitoshi

    1999-01-01

    Nonlinear evolution of single spike structure and vortex in the Richtmyer-Meshkov instability is investigated for two dimensional case, and axial symmetric and non axial symmetric cases with the use of a three-dimensional hydrodynamic code. It is shown that singularity appears in the vorticity left by transmitted and reflected shocks at a corrugated interface. This singularity results in opposite sign of vorticity along the interface that causes double spiral structure of the spike. Difference of nonlinear growth rate and double spiral structure among three cases is also discussed by visualization of simulation data. In a case that there is no slip-off of initial spike axis, vorticity ring is relatively stable, but phase rotation occurs. (author)

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

    Science.gov (United States)

    Churney, Leon; Ohshima, Hisashi

    1963-01-01

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

  3. Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains

    Directory of Open Access Journals (Sweden)

    Rodrigo Cofré

    2018-01-01

    Full Text Available The spiking activity of neuronal networks follows laws that are not time-reversal symmetric; the notion of pre-synaptic and post-synaptic neurons, stimulus correlations and noise correlations have a clear time order. Therefore, a biologically realistic statistical model for the spiking activity should be able to capture some degree of time irreversibility. We use the thermodynamic formalism to build a framework in the context maximum entropy models to quantify the degree of time irreversibility, providing an explicit formula for the information entropy production of the inferred maximum entropy Markov chain. We provide examples to illustrate our results and discuss the importance of time irreversibility for modeling the spike train statistics.

  4. Temporal features of spike trains in the moth antennal lobe revealed by a comparative time-frequency analysis.

    Directory of Open Access Journals (Sweden)

    Alberto Capurro

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

  5. Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model

    Science.gov (United States)

    Panda, Priyadarshini; Srinivasa, Narayan

    2018-01-01

    A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to recognize actions with a limited number of labeled videos. First, we propose a novel encoding, inspired by how microsaccades influence visual perception, to extract spike information from raw video data while preserving the temporal correlation across different frames. Using this encoding, we show that the reservoir generalizes its rich dynamical activity toward signature action/movements enabling it to learn from few training examples. We evaluate our approach on the UCF-101 dataset. Our experiments demonstrate that our proposed reservoir achieves 81.3/87% Top-1/Top-5 accuracy, respectively, on the 101-class data while requiring just 8 video examples per class for training. Our results establish a new benchmark for action recognition from limited video examples for spiking neural models while yielding competitive accuracy with respect to state-of-the-art non-spiking neural models. PMID:29551962

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

    Science.gov (United States)

    Ocker, Gabriel Koch; Doiron, Brent

    2018-02-03

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

  7. Distribution of interspike intervals estimated from multiple spike trains observed in a short time window

    Czech Academy of Sciences Publication Activity Database

    Pawlas, Z.; Lánský, Petr

    2011-01-01

    Roč. 83, č. 1 (2011), e011910 ISSN 1539-3755 R&D Projects: GA MŠk(CZ) LC554; GA ČR(CZ) GAP103/11/0282; GA AV ČR(CZ) IAA101120604 Institutional research plan: CEZ:AV0Z50110509 Keywords : neuron model * estimation * simultanous spike trains Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.255, year: 2011

  8. Detection of bursts in extracellular spike trains using hidden semi-Markov point process models.

    Science.gov (United States)

    Tokdar, Surya; Xi, Peiyi; Kelly, Ryan C; Kass, Robert E

    2010-08-01

    Neurons in vitro and in vivo have epochs of bursting or "up state" activity during which firing rates are dramatically elevated. Various methods of detecting bursts in extracellular spike trains have appeared in the literature, the most widely used apparently being Poisson Surprise (PS). A natural description of the phenomenon assumes (1) there are two hidden states, which we label "burst" and "non-burst," (2) the neuron evolves stochastically, switching at random between these two states, and (3) within each state the spike train follows a time-homogeneous point process. If in (2) the transitions from non-burst to burst and burst to non-burst states are memoryless, this becomes a hidden Markov model (HMM). For HMMs, the state transitions follow exponential distributions, and are highly irregular. Because observed bursting may in some cases be fairly regular-exhibiting inter-burst intervals with small variation-we relaxed this assumption. When more general probability distributions are used to describe the state transitions the two-state point process model becomes a hidden semi-Markov model (HSMM). We developed an efficient Bayesian computational scheme to fit HSMMs to spike train data. Numerical simulations indicate the method can perform well, sometimes yielding very different results than those based on PS.

  9. Kinetic Ductility and Force-Spike Resistance of Proteins from Single-Molecule Force Spectroscopy.

    Science.gov (United States)

    Cossio, Pilar; Hummer, Gerhard; Szabo, Attila

    2016-08-23

    Ductile materials can absorb spikes in mechanical force, whereas brittle ones fail catastrophically. Here we develop a theory to quantify the kinetic ductility of single molecules from force spectroscopy experiments, relating force-spike resistance to the differential responses of the intact protein and the unfolding transition state to an applied mechanical force. We introduce a class of unistable one-dimensional potential surfaces that encompass previous models as special cases and continuously cover the entire range from ductile to brittle. Compact analytic expressions for force-dependent rates and rupture-force distributions allow us to analyze force-clamp and force-ramp pulling experiments. We find that the force-transmitting protein domains of filamin and titin are kinetically ductile when pulled from their two termini, making them resistant to force spikes. For the mechanostable muscle protein titin, a highly ductile model reconciles data over 10 orders of magnitude in force loading rate from experiment and simulation. Copyright © 2016 Biophysical Society. All rights reserved.

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

    Science.gov (United States)

    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.

  11. STDP allows close-to-optimal spatiotemporal spike pattern detection by single coincidence detector neurons.

    Science.gov (United States)

    Masquelier, Timothée

    2017-06-29

    Repeating spatiotemporal spike patterns exist and carry information. How this information is extracted by downstream neurons is unclear. Here we theoretically investigate to what extent a single cell could detect a given spike pattern and what the optimal parameters to do so are, in particular the membrane time constant τ. Using a leaky integrate-and-fire (LIF) neuron with homogeneous Poisson input, we computed this optimum analytically. We found that a relatively small τ (at most a few tens of ms) is usually optimal, even when the pattern is much longer. This is somewhat counter-intuitive as the resulting detector ignores most of the pattern, due to its fast memory decay. Next, we wondered if spike-timing-dependent plasticity (STDP) could enable a neuron to reach the theoretical optimum. We simulated a LIF equipped with additive STDP, and repeatedly exposed it to a given input spike pattern. As in previous studies, the LIF progressively became selective to the repeating pattern with no supervision, even when the pattern was embedded in Poisson activity. Here we show that, using certain STDP parameters, the resulting pattern detector is optimal. These mechanisms may explain how humans learn repeating sensory sequences. Long sequences could be recognized thanks to coincidence detectors working at a much shorter timescale. This is consistent with the fact that recognition is still possible if a sound sequence is compressed, played backward, or scrambled using 10-ms bins. Coincidence detection is a simple yet powerful mechanism, which could be the main function of neurons in the brain. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

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

    Science.gov (United States)

    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.

  14. A Bayesian supervised dual-dimensionality reduction model for simultaneous decoding of LFP and spike train signals.

    Science.gov (United States)

    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.

  15. Informational basis of sensory adaptation: entropy and single-spike efficiency in rat barrel cortex.

    Science.gov (United States)

    Adibi, Mehdi; Clifford, Colin W G; Arabzadeh, Ehsan

    2013-09-11

    We showed recently that exposure to whisker vibrations enhances coding efficiency in rat barrel cortex despite increasing correlations in variability (Adibi et al., 2013). Here, to understand how adaptation achieves this improvement in sensory representation, we decomposed the stimulus information carried in neuronal population activity into its fundamental components in the framework of information theory. In the context of sensory coding, these components are the entropy of the responses across the entire stimulus set (response entropy) and the entropy of the responses conditional on the stimulus (conditional response entropy). We found that adaptation decreased response entropy and conditional response entropy at both the level of single neurons and the pooled activity of neuronal populations. However, the net effect of adaptation was to increase the mutual information because the drop in the conditional entropy outweighed the drop in the response entropy. The information transmitted by a single spike also increased under adaptation. As population size increased, the information content of individual spikes declined but the relative improvement attributable to adaptation was maintained.

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

    Directory of Open Access Journals (Sweden)

    Ceyda eSanli

    2015-09-01

    Full Text Available 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.

  17. Automatic spike sorting for extracellular electrophysiological recording using unsupervised single linkage clustering based on grey relational analysis

    Science.gov (United States)

    Lai, Hsin-Yi; Chen, You-Yin; Lin, Sheng-Huang; Lo, Yu-Chun; Tsang, Siny; Chen, Shin-Yuan; Zhao, Wan-Ting; Chao, Wen-Hung; Chang, Yao-Chuan; Wu, Robby; Shih, Yen-Yu I.; Tsai, Sheng-Tsung; Jaw, Fu-Shan

    2011-06-01

    Automatic spike sorting is a prerequisite for neuroscience research on multichannel extracellular recordings of neuronal activity. A novel spike sorting framework, combining efficient feature extraction and an unsupervised clustering method, is described here. Wavelet transform (WT) is adopted to extract features from each detected spike, and the Kolmogorov-Smirnov test (KS test) is utilized to select discriminative wavelet coefficients from the extracted features. Next, an unsupervised single linkage clustering method based on grey relational analysis (GSLC) is applied for spike clustering. The GSLC uses the grey relational grade as the similarity measure, instead of the Euclidean distance for distance calculation; the number of clusters is automatically determined by the elbow criterion in the threshold-cumulative distribution. Four simulated data sets with four noise levels and electrophysiological data recorded from the subthalamic nucleus of eight patients with Parkinson's disease during deep brain stimulation surgery are used to evaluate the performance of GSLC. Feature extraction results from the use of WT with the KS test indicate a reduced number of feature coefficients, as well as good noise rejection, despite similar spike waveforms. Accordingly, the use of GSLC for spike sorting achieves high classification accuracy in all simulated data sets. Moreover, J-measure results in the electrophysiological data indicating that the quality of spike sorting is adequate with the use of GSLC.

  18. Breaking Bad News Training Program Based on Video Reviews and SPIKES Strategy: What do Perinatology Residents Think about It?

    Science.gov (United States)

    Setubal, Maria Silvia Vellutini; Gonçalves, Andrea Vasconcelos; Rocha, Sheyla Ribeiro; Amaral, Eliana Martorano

    2017-10-01

    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.

  19. Human coronavirus 229E encodes a single ORF4 protein between the spike and the envelope genes

    NARCIS (Netherlands)

    Dijkman, Ronald; Jebbink, Maarten F.; Wilbrink, Berry; Pyrc, Krzysztof; Zaaijer, Hans L.; Minor, Philip D.; Franklin, Sally; Berkhout, Ben; Thiel, Volker; van der Hoek, Lia

    2006-01-01

    BACKGROUND: The genome of coronaviruses contains structural and non-structural genes, including several so-called accessory genes. All group 1b coronaviruses encode a single accessory protein between the spike and envelope genes, except for human coronavirus (HCoV) 229E. The prototype virus has a

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Anja eZai

    2015-10-01

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

  2. Single versus multimodality training basic laparoscopic skills

    NARCIS (Netherlands)

    Brinkman, W.M.; Havermans, S.Y.; Buzink, S.N.; Botden, S.M.B.I.; Jakimowicz, J.J.; Schoot, B.C.

    2012-01-01

    Introduction - Even though literature provides compelling evidence of the value of simulators for training of basic laparoscopic skills, the best way to incorporate them into a surgical curriculum is unclear. This study compares the training outcome of single modality training with multimodality

  3. Generation and characterization of ultra-short electron beams for single spike infrared FEL radiation at SPARC_LAB

    Science.gov (United States)

    Villa, F.; Anania, M. P.; Artioli, M.; Bacci, A.; Bellaveglia, M.; Bisesto, F. G.; Biagioni, A.; Carpanese, M.; Cardelli, F.; Castorina, G.; Chiadroni, E.; Cianchi, A.; Ciocci, F.; Croia, M.; Curcio, A.; Dattoli, G.; Gallo, A.; Di Giovenale, D.; Di Palma, E.; Di Pirro, G.; Ferrario, M.; Filippi, F.; Giannessi, L.; Giribono, A.; Marocchino, A.; Massimo, F.; Mostacci, A.; Petralia, A.; Petrarca, M.; Petrillo, V.; Piersanti, L.; Pioli, S.; Pompili, R.; Romeo, S.; Rossi, A. R.; Scifo, J.; Shpakov, V.; Vaccarezza, C.

    2017-09-01

    The technique for producing and measuring few tens of femtosecond electron beams, and the consequent generation of few tens femtoseconds single spike FEL radiation pulses at SPARC_LAB is presented. The undulator has been used in the double role of radiation source and diagnostic tool for the characterization of the electron beam. The connection between the electron bunch length and the radiation bandwidth is analyzed.

  4. Spike timing precision in the visual front-end

    OpenAIRE

    Borghuis, B.G. (Bart Gerard)

    2003-01-01

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

  5. Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method

    International Nuclear Information System (INIS)

    Nasser, Hassan; Cessac, Bruno; Marre, Olivier

    2013-01-01

    Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main statistics of the data is required. In the first part, we present a review on recent results dealing with spike train statistics analysis using maximum entropy models (MaxEnt). Most of these studies have focused on modelling synchronous spike patterns, leaving aside the temporal dynamics of the neural activity. However, the maximum entropy principle can be generalized to the temporal case, leading to Markovian models where memory effects and time correlations in the dynamics are properly taken into account. In the second part, we present a new method based on Monte Carlo sampling which is suited for the fitting of large-scale spatio-temporal MaxEnt models. The formalism and the tools presented here will be essential to fit MaxEnt spatio-temporal models to large neural ensembles. (paper)

  6. Models of utricular bouton afferents: role of afferent-hair cell connectivity in determining spike train regularity.

    Science.gov (United States)

    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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

  8. Tuning of spinal networks to frequency components of spike trains in individual afferents.

    Science.gov (United States)

    Koerber, H R; Seymour, A W; Mendell, L M

    1991-10-01

    Cord dorsum potentials (CDPs) evoked by primary afferent fiber stimulation reflect the response of postsynaptic dorsal horn neurons. The properties of these CDPs have been shown to vary in accordance with the type of primary afferent fiber stimulated. The purpose of the present study was to determine the relationships between frequency modulation of the afferent input trains, the amplitude modulation of the evoked CDPs, and the type of primary afferent stimulated. The somata of individual primary afferent fibers were impaled in the L7 dorsal root ganglion of alpha-chloralose-anesthetized cats. Action potentials (APs) were evoked in single identified afferents via the intracellular microelectrode while simultaneously recording the response of dorsal horn neurons as CDPs, or activity of individual target interneurons recorded extracellularly or intracellularly. APs were evoked in afferents using temporal patterns identical to the responses of selected afferents to natural stimulation of their receptive fields. Two such physiologically realistic trains, one recorded from a hair follicle and the other from a slowly adapting type 1 receptor, were chosen as standard test trains. Modulation of CDP amplitude in response to this frequency-modulated afferent activity varied according to the type of peripheral mechanoreceptor innervated. Dorsal horn networks driven by A beta afferents innervating hair follicles, rapidly adapting pad (Krause end bulb), and field receptors seemed "tuned" to amplify the onset of activity in single afferents. Networks driven by afferents innervating down hair follicles and pacinian corpuscles required more high-frequency activity to elicit their peak response. Dorsal horn networks driven by afferents innervating slowly adapting receptors including high-threshold mechanoreceptors exhibited some sensitivity to the instantaneous frequency, but in general they reproduced the activity in the afferent fiber much more faithfully. Responses of

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

    Science.gov (United States)

    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

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

    OpenAIRE

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

    2008-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-08-15

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

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

    Science.gov (United States)

    Brock, T C M; Bas, D A; Belgers, J D M; Bibbe, L; Boerwinkel, M-C; Crum, S J H; Diepens, N J; Kraak, M H S; Vonk, J A; Roessink, I

    2016-08-01

    Sediment ecotoxicity studies were conducted with lufenuron to (i) complement the results of a water-spiked mesocosm experiment with this lipophilic benzoylurea insecticide, (ii) to explore the predictive value of laboratory single-species tests for population and community-level responses of benthic macroinvertebrates, and (iii) to calibrate the tier-1 effect assessment procedure for sediment organisms. For this purpose the concentration-response relationships for macroinvertebrates between sediment-spiked microcosms and those of 28-d sediment-spiked single-species toxicity tests with Chironomus riparius, Hyalella azteca and Lumbriculus variegatus were compared. Lufenuron persisted in the sediment of the microcosms. On average, 87.7% of the initial lufenuron concentration could still be detected in the sediment after 12 weeks. Overall, benthic insects and crustaceans showed treatment-related declines and oligochaetes treatment-related increases. The lowest population-level NOEC in the microcosms was 0.79μg lufenuron/g organic carbon in dry sediment (μg a.s./g OC) for Tanytarsini, Chironomini and Dero sp. Multivariate analysis of the responses of benthic macroinvertebrates revealed a community-level NOEC of 0.79μg a.s./g OC. The treatment-related responses observed in the microcosms are in accordance with the results of the 28-d laboratory toxicity tests. These tests showed that the insect C. riparius and the crustacean H. azteca were approximately two orders of magnitude more sensitive than the oligochaete L. variegatus. In our laboratory tests, using field-collected sediment, the lowest 28-d EC10 (0.49μg a.s./g OC) was observed for C. riparius (endpoint survival), while for the standard OECD test with this species, using artificial sediment, a NOEC of 2.35μg a.s./g OC (endpoint emergence) is reported. In this particular case, the sediment tier-1 effect assessment using the chronic EC10 (field-collected sediment) or chronic NOEC (artificial sediment) of C

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

    Science.gov (United States)

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

    2011-01-01

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

  14. Operant conditioning of synaptic and spiking activity patterns in single hippocampal neurons.

    Science.gov (United States)

    Ishikawa, Daisuke; Matsumoto, Nobuyoshi; Sakaguchi, Tetsuya; Matsuki, Norio; Ikegaya, Yuji

    2014-04-02

    Learning is a process of plastic adaptation through which a neural circuit generates a more preferable outcome; however, at a microscopic level, little is known about how synaptic activity is patterned into a desired configuration. Here, we report that animals can generate a specific form of synaptic activity in a given neuron in the hippocampus. In awake, head-restricted mice, we applied electrical stimulation to the lateral hypothalamus, a reward-associated brain region, when whole-cell patch-clamped CA1 neurons exhibited spontaneous synaptic activity that met preset criteria. Within 15 min, the mice learned to generate frequently the excitatory synaptic input pattern that satisfied the criteria. This reinforcement learning of synaptic activity was not observed for inhibitory input patterns. When a burst unit activity pattern was conditioned in paired and nonpaired paradigms, the frequency of burst-spiking events increased and decreased, respectively. The burst reinforcement occurred in the conditioned neuron but not in other adjacent neurons; however, ripple field oscillations were concomitantly reinforced. Neural conditioning depended on activation of NMDA receptors and dopamine D1 receptors. Acutely stressed mice and depression model mice that were subjected to forced swimming failed to exhibit the neural conditioning. This learning deficit was rescued by repetitive treatment with fluoxetine, an antidepressant. Therefore, internally motivated animals are capable of routing an ongoing action potential series into a specific neural pathway of the hippocampal network.

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

    Science.gov (United States)

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

    2018-01-01

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

  16. A prolongation of the postspike afterhyperpolarization following spike trains can partly explain the lower firing rates at derecruitment than those at recruitment

    DEFF Research Database (Denmark)

    Wienecke, Jacob; Zhang, Mengliang; Hultborn, Hans

    2009-01-01

    for the lower frequencies at derecruitment. This was independent of whether the current injection had activated persistent inward current (PIC; plateau potentials, secondary range firing). It was found that a preceding spike train could prolong the AHP duration following a subsequent spike. The lower rate...... from AHP duration in fast motoneurons and higher than expected in slow motoneurons. It is suggested that these deviations are explained by the presence of synaptic noise as well as recruitment of PICs below firing threshold. Thus synaptic noise may allow spike discharge even after the end of the AHP...... in "fast" motor neurons, whereas synaptic noise and PICs below spike threshold tend to give higher minimum firing frequencies in "slow" motor neurons than predicted from AHP duration....

  17. Memristors Empower Spiking Neurons With Stochasticity

    KAUST Repository

    Al-Shedivat, Maruan

    2015-06-01

    Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms. © 2011 IEEE.

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

    Science.gov (United States)

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

    2010-11-17

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

  19. Multiple-color optical activation, silencing, and desynchronization of neural activity, with single-spike temporal resolution.

    Directory of Open Access Journals (Sweden)

    Xue Han

    Full Text Available The quest to determine how precise neural activity patterns mediate computation, behavior, and pathology would be greatly aided by a set of tools for reliably activating and inactivating genetically targeted neurons, in a temporally precise and rapidly reversible fashion. Having earlier adapted a light-activated cation channel, channelrhodopsin-2 (ChR2, for allowing neurons to be stimulated by blue light, we searched for a complementary tool that would enable optical neuronal inhibition, driven by light of a second color. Here we report that targeting the codon-optimized form of the light-driven chloride pump halorhodopsin from the archaebacterium Natronomas pharaonis (hereafter abbreviated Halo to genetically-specified neurons enables them to be silenced reliably, and reversibly, by millisecond-timescale pulses of yellow light. We show that trains of yellow and blue light pulses can drive high-fidelity sequences of hyperpolarizations and depolarizations in neurons simultaneously expressing yellow light-driven Halo and blue light-driven ChR2, allowing for the first time manipulations of neural synchrony without perturbation of other parameters such as spiking rates. The Halo/ChR2 system thus constitutes a powerful toolbox for multichannel photoinhibition and photostimulation of virally or transgenically targeted neural circuits without need for exogenous chemicals, enabling systematic analysis and engineering of the brain, and quantitative bioengineering of excitable cells.

  20. Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25ms to 125µs

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    Nan eDu

    2015-06-01

    Full Text Available Memristive devices are popular among neuromorphic engineers for their ability to emulate forms of spike-driven synaptic plasticity by applying specific voltage and current waveforms at their two terminals. In this paper, we investigate spike-timing dependent plasticity (STDP with a single pairing of one presynaptic voltage spike and one postsynaptic voltage spike in a BiFeO3 memristive device. In most memristive materials the learning window is primarily a function of the material characteristics and not of the applied waveform. In contrast, we show that the analog resistive switching of the developed artificial synapses allows to adjust the learning time constant of the STDP function from 25ms to 125μs via the duration of applied voltage spikes. Also, as the induced weight change may degrade, we investigate the remanence of the resistance change for several hours after analog resistive switching, thus emulating the processes expected in biological synapses. As the power consumption is a major constraint in neuromorphic circuits, we show methods to reduce the consumed energy per setting pulse to only 4.5 pJ in the developed artificial synapses.

  1. Transfer in motion discrimination learning was no greater in double training than in single training.

    Science.gov (United States)

    Huang, Jinfeng; Liang, Ju; Zhou, Yifeng; Liu, Zili

    2017-06-01

    We investigated the controversy regarding double training in motion discrimination learning. We collected data from 43 participants in a motion direction discrimination learning task with either double training (i.e., training plus exposure) or single training (i.e., no exposure). By pooling these data with those in the literature, we had data in double training from 28 participants and in single training from 36 participants. We found that, in double training, the transfer along the exposed direction was less than that along the trained direction, indicating incomplete transfer. Importantly, the transfer in double training was not reliably greater than that in single training.

  2. Predictive coding of dynamical variables in balanced spiking networks.

    Science.gov (United States)

    Boerlin, Martin; Machens, Christian K; Denève, Sophie

    2013-01-01

    Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.

  3. Generation of a comb electron beam to drive SASE FEL radiation spikes

    International Nuclear Information System (INIS)

    Boscolo, M.; Boscolo, I.; Castelli, F.; Cialdi, S.; Ferrario, M.; Petrillo, V.; Vaccarezza, C.

    2008-01-01

    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

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

    Directory of Open Access Journals (Sweden)

    Laureline Logiaco

    2015-08-01

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

  5. Bursts generate a non-reducible spike-pattern code

    Directory of Open Access Journals (Sweden)

    Hugo G Eyherabide

    2009-05-01

    Full Text Available On the single-neuron level, precisely timed spikes can either constitute firing-rate codes or spike-pattern codes that utilize the relative timing between consecutive spikes. There has been little experimental support for the hypothesis that such temporal patterns contribute substantially to information transmission. Using grasshopper auditory receptors as a model system, we show that correlations between spikes can be used to represent behaviorally relevant stimuli. The correlations reflect the inner structure of the spike train: a succession of burst-like patterns. We demonstrate that bursts with different spike counts encode different stimulus features, such that about 20% of the transmitted information corresponds to discriminating between different features, and the remaining 80% is used to allocate these features in time. In this spike-pattern code, the "what" and the "when" of the stimuli are encoded in the duration of each burst and the time of burst onset, respectively. Given the ubiquity of burst firing, we expect similar findings also for other neural systems.

  6. Automatic fitting of spiking neuron models to electrophysiological recordings

    Directory of Open Access Journals (Sweden)

    Cyrille Rossant

    2010-03-01

    Full Text Available Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains that can run in parallel on graphics processing units (GPUs. The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.

  7. Glutamate Excitotoxicity Is Involved in the Induction of Paralysis in Mice after Infection by a Human Coronavirus with a Single Point Mutation in Its Spike Protein▿

    Science.gov (United States)

    Brison, Elodie; Jacomy, Hélène; Desforges, Marc; Talbot, Pierre J.

    2011-01-01

    Human coronaviruses (HCoV) are recognized respiratory pathogens, and some strains, including HCoV-OC43, can infect human neuronal and glial cells of the central nervous system (CNS) and activate neuroinflammatory mechanisms. Moreover, HCoV-OC43 is neuroinvasive, neurotropic, and neurovirulent in susceptible mice, where it induces chronic encephalitis. Herein, we show that a single point mutation in the viral spike (S) glycoprotein (Y241H), acquired during viral persistence in human neural cells, led to a hind-limb paralytic disease in infected mice. Inhibition of glutamate excitotoxicity using a 2-amino-3-(5-methyl-3-oxo-1,2-oxazol-4-yl)propranoic acid (AMPA) receptor antagonist (GYKI-52466) improved clinical scores related to the paralysis and motor disabilities in S mutant virus-infected mice, as well as protected the CNS from neuronal dysfunctions, as illustrated by restoration of the phosphorylation state of neurofilaments. Expression of the glial glutamate transporter GLT-1, responsible for glutamate homeostasis, was downregulated following infection, and GYKI-52466 also significantly restored its steady-state expression level. Finally, GYKI-52466 treatment of S mutant virus-infected mice led to reduced microglial activation, which may lead to improvement in the regulation of CNS glutamate homeostasis. Taken together, our results strongly suggest an involvement of excitotoxicity in the paralysis-associated neuropathology induced by an HCoV-OC43 mutant which harbors a single point mutation in its spike protein that is acquired upon persistent virus infection. PMID:21957311

  8. Information transmission with spiking Bayesian neurons

    International Nuclear Information System (INIS)

    Lochmann, Timm; Deneve, Sophie

    2008-01-01

    Spike trains of cortical neurons resulting from repeatedpresentations of a stimulus are variable and exhibit Poisson-like statistics. Many models of neural coding therefore assumed that sensory information is contained in instantaneous firing rates, not spike times. Here, we ask how much information about time-varying stimuli can be transmitted by spiking neurons with such input and output variability. In particular, does this variability imply spike generation to be intrinsically stochastic? We consider a model neuron that estimates optimally the current state of a time-varying binary variable (e.g. presence of a stimulus) by integrating incoming spikes. The unit signals its current estimate to other units with spikes whenever the estimate increased by a fixed amount. As shown previously, this computation results in integrate and fire dynamics with Poisson-like output spike trains. This output variability is entirely due to the stochastic input rather than noisy spike generation. As a result such a deterministic neuron can transmit most of the information about the time varying stimulus. This contrasts with a standard model of sensory neurons, the linear-nonlinear Poisson (LNP) model which assumes that most variability in output spike trains is due to stochastic spike generation. Although it yields the same firing statistics, we found that such noisy firing results in the loss of most information. Finally, we use this framework to compare potential effects of top-down attention versus bottom-up saliency on information transfer with spiking neurons

  9. Implications of Impaired Endurance Performance following Single Bouts of Resistance Training: An Alternate Concurrent Training Perspective.

    Science.gov (United States)

    Doma, Kenji; Deakin, Glen B; Bentley, David J

    2017-11-01

    A single bout of resistance training induces residual fatigue, which may impair performance during subsequent endurance training if inadequate recovery is allowed. From a concurrent training standpoint, such carry-over effects of fatigue from a resistance training session may impair the quality of a subsequent endurance training session for several hours to days with inadequate recovery. The proposed mechanisms of this phenomenon include: (1) impaired neural recruitment patterns; (2) reduced movement efficiency due to alteration in kinematics during endurance exercise and increased energy expenditure; (3) increased muscle soreness; and (4) reduced muscle glycogen. If endurance training quality is consistently compromised during the course of a specific concurrent training program, optimal endurance development may be limited. Whilst the link between acute responses of training and subsequent training adaptation has not been fully established, there is some evidence suggesting that cumulative effects of fatigue may contribute to limiting optimal endurance development. Thus, the current review will (1) explore cross-sectional studies that have reported impaired endurance performance following a single, or multiple bouts, of resistance training; (2) identify the potential impact of fatigue on chronic endurance development; (3) describe the implications of fatigue on the quality of endurance training sessions during concurrent training, and (4) explain the mechanisms contributing to resistance training-induced attenuation on endurance performance from neurological, biomechanical and metabolic standpoints. Increasing the awareness of resistance training-induced fatigue may encourage coaches to consider modulating concurrent training variables (e.g., order of training mode, between-mode recovery period, training intensity, etc.) to limit the carry-over effects of fatigue from resistance to endurance training sessions.

  10. Improved SpikeProp for Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Falah Y. H. Ahmed

    2013-01-01

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

  11. Breaking bad news education for emergency medicine residents: A novel training module using simulation with the SPIKES protocol

    OpenAIRE

    Park, Inchoel; Gupta, Amit; Mandani, Kaivon; Haubner, Laura; Peckler, Brad

    2010-01-01

    Breaking bad news (BBN) in the emergency department (ED) is a common occurrence. This is especially true for an emergency physician (EP) as there is little time to prepare for the event and likely little or no knowledge of the patients or family background information. At our institution, there is no formal training for EP residents in delivering bad news. We felt teaching emergency medicine residents these communication skills should be an important part of their educational curriculum. We d...

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

    Directory of Open Access Journals (Sweden)

    Takashi eTakekawa

    2012-03-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  14. A biomarker model of sublethal genotoxicity (DNA single-strand breaks and adducts) using the sentinel organism Aporrectodea longa in spiked soil

    International Nuclear Information System (INIS)

    Martin, Francis L.; Piearce, Trevor G.; Hewer, Alan; Phillips, David H.; Semple, Kirk T.

    2005-01-01

    There is a need to develop risk biomarkers during the remediation of contaminated land. We employed the earthworm, Aporrectodea longa (Ude), to determine whether genotoxicity measures could be applied to this organism's intestinal tissues. Earthworms were added, for 24 h or 7 days, to soil samples spiked with benzo[a]pyrene (B[a]P) and/or lindane. After exposure, intestinal tissues (crop/gizzard or intestine) were removed prior to the measurement in disaggregated cells of DNA single-strand breaks (SSBs) by the alkaline comet assay. Damage was quantified by comet tail length (CTL, μm). B[a]P 24-h exposure induced dose-related increases (P 32 P-postlabelling, showed a two-adduct-spot pattern. This preliminary investigation suggests that earthworm tissues may be incorporated into genotoxicity assays to facilitate hazard identification within terrestrial ecosystems. - Sublethal genotoxicity in the sentinel organism A. longa can be used to monitor the effects of contaminants in soil

  15. Stochastic Variational Learning in Recurrent Spiking Networks

    Directory of Open Access Journals (Sweden)

    Danilo eJimenez Rezende

    2014-04-01

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

  16. Breaking bad news education for emergency medicine residents: A novel training module using simulation with the SPIKES protocol

    Directory of Open Access Journals (Sweden)

    Park Inchoel

    2010-01-01

    Full Text Available Breaking bad news (BBN in the emergency department (ED is a common occurrence. This is especially true for an emergency physician (EP as there is little time to prepare for the event and likely little or no knowledge of the patients or family background information. At our institution, there is no formal training for EP residents in delivering bad news. We felt teaching emergency medicine residents these communication skills should be an important part of their educational curriculum. We describe our experience with a defined educational program designed to educate and improve physician′s confidence and competence in bad news and death notification. A regularly scheduled 5-h grand rounds conference time frame was dedicated to the education of EM residents about BBN. A multidisciplinary approach was taken to broaden the prospective of the participants. The course included lectures from different specialties, role playing for three short scenarios in different capacities, and hi-fidelity simulation cases with volatile psychosocial issues and stressors. Participants were asked to fill out a self-efficacy form and evaluation sheets. Fourteen emergency residents participated and all thought that this education is necessary. The mean score of usefulness is 4.73 on a Likert Scale from 1 to 5. The simulation part was thought to be the most useful (43%, with role play 14%, and lecture 7%. We believe that teaching physicians to BBN in a controlled environment is a good use of educational time and an important procedure that EP must learn.

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

    OpenAIRE

    Bi, Zedong; Zhou, Changsong

    2016-01-01

    In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons and synapses as well as the randomness of connection details, spike trains typically exhibit variability such as spatial randomness and temporal stochasticity, resulting in variability of synaptic changes under plasticity, which we call efficacy variability. How the variability of spike trains influences the efficacy variability of synapses remains unclear. In this paper, we try to...

  18. Deep Spiking Networks

    NARCIS (Netherlands)

    O'Connor, P.; Welling, M.

    2016-01-01

    We introduce an algorithm to do backpropagation on a spiking network. Our network is "spiking" in the sense that our neurons accumulate their activation into a potential over time, and only send out a signal (a "spike") when this potential crosses a threshold and the neuron is reset. Neurons only

  19. Spiking neural circuits with dendritic stimulus processors : encoding, decoding, and identification in reproducing kernel Hilbert spaces.

    Science.gov (United States)

    Lazar, Aurel A; Slutskiy, Yevgeniy B

    2015-02-01

    We present a multi-input multi-output neural circuit architecture for nonlinear processing and encoding of stimuli in the spike domain. In this architecture a bank of dendritic stimulus processors implements nonlinear transformations of multiple temporal or spatio-temporal signals such as spike trains or auditory and visual stimuli in the analog domain. Dendritic stimulus processors may act on both individual stimuli and on groups of stimuli, thereby executing complex computations that arise as a result of interactions between concurrently received signals. The results of the analog-domain computations are then encoded into a multi-dimensional spike train by a population of spiking neurons modeled as nonlinear dynamical systems. We investigate general conditions under which such circuits faithfully represent stimuli and demonstrate algorithms for (i) stimulus recovery, or decoding, and (ii) identification of dendritic stimulus processors from the observed spikes. Taken together, our results demonstrate a fundamental duality between the identification of the dendritic stimulus processor of a single neuron and the decoding of stimuli encoded by a population of neurons with a bank of dendritic stimulus processors. This duality result enabled us to derive lower bounds on the number of experiments to be performed and the total number of spikes that need to be recorded for identifying a neural circuit.

  20. Neuronal coding and spiking randomness

    Czech Academy of Sciences Publication Activity Database

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

    2007-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Guillaume eLajoie

    2014-10-01

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

  2. Spike Bursts from an Excitable Optical System

    Science.gov (United States)

    Rios Leite, Jose R.; Rosero, Edison J.; Barbosa, Wendson A. S.; Tredicce, Jorge R.

    Diode Lasers with double optical feedback are shown to present power drop spikes with statistical distribution controllable by the ratio of the two feedback times. The average time between spikes and the variance within long time series are studied. The system is shown to be excitable and present bursting of spikes created with specific feedback time ratios and strength. A rate equation model, extending the Lang-Kobayashi single feedback for semiconductor lasers proves to match the experimental observations. Potential applications to construct network to mimic neural systems having controlled bursting properties in each unit will be discussed. Brazilian Agency CNPQ.

  3. Span: spike pattern association neuron for learning spatio-temporal spike patterns.

    Science.gov (United States)

    Mohemmed, Ammar; Schliebs, Stefan; Matsuda, Satoshi; Kasabov, Nikola

    2012-08-01

    Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN - a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the precise timing of spikes. The idea of the proposed algorithm is to transform spike trains during the learning phase into analog signals so that common mathematical operations can be performed on them. Using this conversion, it is possible to apply the well-known Widrow-Hoff rule directly to the transformed spike trains in order to adjust the synaptic weights and to achieve a desired input/output spike behavior of the neuron. In the presented experimental analysis, the proposed learning algorithm is evaluated regarding its learning capabilities, its memory capacity, its robustness to noisy stimuli and its classification performance. Differences and similarities of SPAN regarding two related algorithms, ReSuMe and Chronotron, are discussed.

  4. Factors correlated with volleyball spike velocity.

    Science.gov (United States)

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

    2005-10-01

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

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

    Directory of Open Access Journals (Sweden)

    Oliynyk Andriy

    2012-08-01

    Full Text Available Abstract Background Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Results Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting, which is designed to optimize: (i fast and accurate detection, (ii offline sorting and (iii online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com using LabVIEW (National Instruments, USA. We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is

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

    Science.gov (United States)

    Oliynyk, Andriy; Bonifazzi, Claudio; Montani, Fernando; Fadiga, Luciano

    2012-08-08

    Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike

  7. Training-induced brain activation and functional connectivity differentiate multi-talker and single-talker speech training.

    Science.gov (United States)

    Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C M

    2018-03-10

    In second language acquisition studies, the high talker variability training approach has been frequently used to train participants to learn new speech patterns. However, the neuroplasticity induced by training is poorly understood. In the present study, native English speakers were trained on non-native pitch patterns (linguistic tones from Mandarin Chinese) in multi-talker (N = 16) or single-talker (N = 16) training conditions. We focused on two aspects of multi-talker training, voice processing and lexical phonology accessing, and used functional magnetic resonance imaging (fMRI) to measure the brain activation and functional connectivity (FC) of two regions of interest in a tone identification task conducted before and after training, namely the anterior part of the right superior temporal gyrus (aRSTG) and the posterior left superior temporal gyrus (pLSTG). The results showed distinct patterns of associations between neural signals and learning success for multi-talker training. Specifically, post-training brain activation in the aRSTG and FC strength between the aRSTG and pLSTG were correlated with learning success in the multi-talker training group but not in the single-talker group. These results suggest that talker variability in the training procedure may enhance neural efficiency in these brain areas and strengthen the cooperation between them. Our findings highlight the brain processing of newly learned speech patterns is influenced by the given training approach. Copyright © 2018 Elsevier Inc. All rights reserved.

  8. Barbed micro-spikes for micro-scale biopsy

    Science.gov (United States)

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

    2005-06-01

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

  9. Interpreting Adaptation to Concurrent Compared with Single-Mode Exercise Training: Some Methodological Considerations.

    Science.gov (United States)

    Fyfe, Jackson J; Loenneke, Jeremy P

    2018-02-01

    Incorporating both endurance and resistance training into an exercise regime is termed concurrent training. While there is evidence that concurrent training can attenuate resistance training-induced improvements in maximal strength and muscle hypertrophy, research findings are often equivocal, with some suggesting short-term concurrent training may instead further enhance muscle hypertrophy versus resistance training alone. These observations have questioned the validity of the purported 'interference effect' on muscle hypertrophy with concurrent versus single-mode resistance training. This article aims to highlight some methodological considerations when interpreting the concurrent training literature, and, in particular, the degree of changes in strength and muscle hypertrophy observed with concurrent versus single-mode resistance training. Individual training status clearly influences the relative magnitude and specificity of both training adaptation and post-exercise molecular responses in skeletal muscle. The training status of participants is therefore likely a key modulator of the degree of adaptation and interference seen with concurrent training interventions. The divergent magnitudes of strength gain versus muscle hypertrophy induced by resistance training also suggests most concurrent training studies are likely to observe more substantial changes in (and in turn, any potential interference to) strength compared with muscle hypertrophy. Both the specificity and sensitivity of measures used to assess training-induced changes in strength and muscle hypertrophy also likely influence the interpretation of concurrent training outcomes. Finally, the relative importance of any modulation of hypertrophic versus strength adaptation with concurrent training should be considered in context with the relevance of training-induced changes in these variables for enhancing athletic performance and/or functional capacity. Taken together, these observations suggest that

  10. Prospective Coding by Spiking Neurons.

    Directory of Open Access Journals (Sweden)

    Johanni Brea

    2016-06-01

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

  11. Effects of single- vs. multiple-set resistance training on maximum strength and body composition in trained postmenopausal women.

    Science.gov (United States)

    Kemmler, Wolfgang K; Lauber, Dirk; Engelke, Klaus; Weineck, Juergen

    2004-11-01

    The purpose of this study was to examine the effect of a single- vs. a multiple-set resistance training protocol in well-trained early postmenopausal women. Subjects (N = 71) were randomly assigned to begin either with 12 weeks of the single-set or 12 weeks of the multiple-set protocol. After another 5 weeks of regenerational resistance training, the subgroup performing the single-set protocol during the first 12 weeks crossed over to the 12-week multiple-set protocol and vice versa. Neither exercise type nor exercise intensity, degree of fatigue, rest periods, speed of movement, training sessions per week, compliance and attendance, or periodization strategy differed between exercise protocols. Body mass, body composition, and 1 repetition maximum (1RM) values for leg press, bench press, rowing, and leg adduction were measured at baseline and after each period. Multiple-set training resulted in significant increases (3.5-5.5%) for all 4 strength measurements, whereas single-set training resulted in significant decreases (-1.1 to -2.0%). Body mass and body composition did not change during the study. The results show that, in pretrained subjects, multiple-set protocols are superior to single-set protocols in increasing maximum strength.

  12. Spike correlations in a songbird agree with a simple markov population model.

    Directory of Open Access Journals (Sweden)

    Andrea P Weber

    2007-12-01

    Full Text Available 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.

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

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2017-06-01

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

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

    OpenAIRE

    Swindale, Nicholas V.; Spacek, Martin A.

    2014-01-01

    In order to determine patterns of neural activity, spike signals recorded by extracellular electrodes have to be clustered (sorted) with the aim of ensuring that each cluster represents all the spikes generated by an individual neuron. Many methods for spike sorting have been proposed but few are easily applicable to recordings from polytrodes which may have 16 or more recording sites. As with tetrodes, these are spaced sufficiently closely that signals from single neurons will usually be rec...

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

    OpenAIRE

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

    2015-01-01

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

  16. A new supervised learning algorithm for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

  17. A single amino acid substitution in the S1 and S2 Spike protein domains determines the neutralization escape phenotype of SARS-CoV.

    Science.gov (United States)

    Mitsuki, Yu-ya; Ohnishi, Kazuo; Takagi, Hirotaka; Oshima, Masamichi; Yamamoto, Takuya; Mizukoshi, Fuminori; Terahara, Kazutaka; Kobayashi, Kazuo; Yamamoto, Naoki; Yamaoka, Shoji; Tsunetsugu-Yokota, Yasuko

    2008-07-01

    In response to SARS-CoV infection, neutralizing antibodies are generated against the Spike (S) protein. Determination of the active regions that allow viral escape from neutralization would enable the use of these antibodies for future passive immunotherapy. We immunized mice with UV-inactivated SARS-CoV to generate three anti-S monoclonal antibodies, and established several neutralization escape mutants with S protein. We identified several amino acid substitutions, including Y442F and V601G in the S1 domain and D757N and A834V in the S2 region. In the presence of each neutralizing antibody, double mutants with substitutions in both domains exhibited a greater growth advantage than those with only one substitution. Importantly, combining two monoclonal antibodies that target different epitopes effected almost complete suppression of wild type virus replication. Thus, for effective passive immunotherapy, it is important to use neutralizing antibodies that recognize both the S1 and S2 regions.

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

    Directory of Open Access Journals (Sweden)

    Ashok Litwin-Kumar

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

  19. The variational spiked oscillator

    International Nuclear Information System (INIS)

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

    1992-08-01

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

  20. Solving constraint satisfaction problems with networks of spiking neurons

    Directory of Open Access Journals (Sweden)

    Zeno eJonke

    2016-03-01

    Full Text Available Network of neurons in the brain apply – unlike processors in our current generation ofcomputer hardware – an event-based processing strategy, where short pulses (spikes areemitted sparsely by neurons to signal the occurrence of an event at a particular point intime. Such spike-based computations promise to be substantially more power-efficient thantraditional clocked processing schemes. However it turned out to be surprisingly difficult todesign networks of spiking neurons that can solve difficult computational problems on the levelof single spikes (rather than rates of spikes. We present here a new method for designingnetworks of spiking neurons via an energy function. Furthermore we show how the energyfunction of a network of stochastically firing neurons can be shaped in a quite transparentmanner by composing the networks of simple stereotypical network motifs. We show that thisdesign approach enables networks of spiking neurons to produce approximate solutions todifficult (NP-hard constraint satisfaction problems from the domains of planning/optimizationand verification/logical inference. The resulting networks employ noise as a computationalresource. Nevertheless the timing of spikes (rather than just spike rates plays an essential rolein their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines and Gibbs sampling.

  1. Conversion of the dual training aircraft (DC into single control advanced training aircraft (SC. Part I

    Directory of Open Access Journals (Sweden)

    Ioan ŞTEFĂNESCU

    2011-03-01

    Full Text Available Converting the DC school jet aircraft into SC advanced training aircraft - and use them forthe combat training of military pilots from the operational units, has become a necessity due to thebudget cuts for Air Force, with direct implications on reducing the number of hours of flight assignedto operating personnel for preparing and training.The purpose of adopting such a program is to reduce the number of flight hours allocated annuallyfor preparing and training in advanced stages of instruction, for every pilot, by more intensive use ofthis type of aircraft, which has the advantage of lower flight hour costs as compared to a supersoniccombat plane.

  2. Response Features Determining Spike Times

    Directory of Open Access Journals (Sweden)

    Barry J. Richmond

    1999-01-01

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

  3. Spiking neural network for recognizing spatiotemporal sequences of spikes

    International Nuclear Information System (INIS)

    Jin, Dezhe Z.

    2004-01-01

    Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons

  4. Precise-Spike-Driven Synaptic Plasticity: Learning Hetero-Association of Spatiotemporal Spike Patterns

    Science.gov (United States)

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

    2013-01-01

    A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe. PMID:24223789

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

    Directory of Open Access Journals (Sweden)

    Qiang Yu

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

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

    Stern, Estee; García-Crescioni, Keyla; Miller, Mark W; Peskin, Charles S; Brezina, Vladimir

    2009-11-15

    Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.

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

    DEFF Research Database (Denmark)

    Kardos, J; Szilágyi, N; Juhász, G

    1998-01-01

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

  9. Self-Administered, Home-Based SMART (Sensorimotor Active Rehabilitation Training) Arm Training: A Single-Case Report.

    Science.gov (United States)

    Hayward, Kathryn S; Neibling, Bridee A; Barker, Ruth N

    2015-01-01

    This single-case, mixed-method study explored the feasibility of self-administered, home-based SMART (sensorimotor active rehabilitation training) Arm training for a 57-yr-old man with severe upper-limb disability after a right frontoparietal hemorrhagic stroke 9 mo earlier. Over 4 wk of self-administered, home-based SMART Arm training, the participant completed 2,100 repetitions unassisted. His wife provided support for equipment set-up and training progressions. Clinically meaningful improvements in arm impairment (strength), activity (arm and hand tasks), and participation (use of arm in everyday tasks) occurred after training (at 4 wk) and at follow-up (at 16 wk). Areas for refinement of SMART Arm training derived from thematic analysis of the participant's and researchers' journals focused on enabling independence, ensuring home and user friendliness, maintaining the motivation to persevere, progressing toward everyday tasks, and integrating practice into daily routine. These findings suggest that further investigation of self-administered, home-based SMART Arm training is warranted for people with stroke who have severe upper-limb disability. Copyright © 2015 by the American Occupational Therapy Association, Inc.

  10. Spiking Neurons for Analysis of Patterns

    Science.gov (United States)

    Huntsberger, Terrance

    2008-01-01

    Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological

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

    Science.gov (United States)

    Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

    2016-08-01

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

  12. A spike-timing code for discriminating conspecific vocalizations in the thalamocortical system of anesthetized and awake guinea pigs.

    Science.gov (United States)

    Huetz, Chloé; Philibert, Bénédicte; Edeline, Jean-Marc

    2009-01-14

    Understanding how communication sounds are processed and encoded in the central auditory system is critical to understanding the neural bases of acoustic communication. Here, we examined neuronal representations of species-specific vocalizations, which are communication sounds that many species rely on for survival and social interaction. In some species, the evoked responses of auditory cortex neurons are stronger in response to natural conspecific vocalizations than to their time-reversed, spectrally identical, counterparts. We applied information theory-based analyses to single-unit spike trains collected in the auditory cortex (n = 139) and auditory thalamus (n = 135) of anesthetized animals as well as in the auditory cortex (n = 119) of awake guinea pigs during presentation of four conspecific vocalizations. Few thalamic and cortical cells (information transmitted by the spike trains was quantified with a temporal precision of 10-50 ms, many cells (>75%) displayed a significant amount of information (i.e., >2SD above chance levels), especially in the awake condition. The computed correlation index between spike trains (R(corr), defined by Schreiber et al., 2003) indicated similar spike-timing reliability for both the natural and time-reversed versions of each vocalization, but higher reliability for awake animals compared with anesthetized animals. Based on temporal discharge patterns, even cells that were only weakly responsive to vocalizations displayed a significant level of information. These findings emphasize the importance of temporal discharge patterns as a coding mechanism for natural communication sounds, particularly in awake animals.

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

    Science.gov (United States)

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

    2002-12-01

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

  14. Spiking Neuron Network Helmholtz Machine

    Directory of Open Access Journals (Sweden)

    Pavel eSountsov

    2015-04-01

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

  15. Dynamic response analysis of single-span guideway caused by high speed maglev train

    Directory of Open Access Journals (Sweden)

    Jin Shi

    Full Text Available High speed maglev is one of the most important reformations in the ground transportation systems because of its no physical contact nature. This paper intends to study the dynamic response of the single-span guideway induced by moving maglev train. The dynamic model of the maglev train-guideway system is established. In this model, a maglev train consists of three vehicles and each vehicle is regarded as a multibody system with 34 degrees-of-freedom. The guideway is modeled as a simply supported beam. Considering the motion-dependent nature of electromagnetic forces in the maglev system, an iterative approach is presented to compute the dynamic response of a maglev train-guideway system. The histories of the train traversing the guideways are simulated and the dynamic responses of the guideway and the train vehicles are calculated. A field experiment is carried out to verify the results of the analysis. The resonant conditions of single-span guideway are analyzed. The results show that all the dynamic indexes of train-guideway system are far less than permissive values of railway and maglev system, the vertical resonant of guideways caused by periodical excitations of the train will not happen.

  16. Neuronal spike sorting based on radial basis function neural networks

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

    Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

  17. Direct Training to Improve Educators' Treatment Integrity: A Systematic Review of Single-Case Design Studies.

    Science.gov (United States)

    Fallon, Lindsay M; Kurtz, Kathryn D; Mueller, Marlana R

    2017-06-15

    In consultation, school psychologists may offer educators direct training to support the implementation of classroom interventions aimed to improve student outcomes. The purpose of this study was to conduct a systematic literature review of single-case design research studies during which educators received direct training to implement a classroom intervention, specifically instructions, modeling, practice, and feedback. Two doctoral students in school psychology screened 228 articles and evaluated 33 studies to determine if direct training is effective and an evidence-based practice per single-case design standards proposed by the What Works Clearinghouse. Results of the review indicate that there is support for the practice to be deemed evidence-based and associated with better intervention implementation than before its application. Implications include direct training being considered for intensive, complex interventions to promote educator success with implementation. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  18. Single Mutations in the VP2 300 Loop Region of the Three-Fold Spike of the Carnivore Parvovirus Capsid Can Determine Host Range

    Science.gov (United States)

    Organtini, Lindsey J.; Zhang, Sheng; Hafenstein, Susan L.; Holmes, Edward C.

    2015-01-01

    ABSTRACT Sylvatic carnivores, such as raccoons, have recently been recognized as important hosts in the evolution of canine parvovirus (CPV), a pandemic pathogen of domestic dogs. Although viruses from raccoons do not efficiently bind the dog transferrin receptor (TfR) or infect dog cells, a single mutation changing an aspartic acid to a glycine at capsid (VP2) position 300 in the prototype raccoon CPV allows dog cell infection. Because VP2 position 300 exhibits extensive amino acid variation among the carnivore parvoviruses, we further investigated its role in determining host range by analyzing its diversity and evolution in nature and by creating a comprehensive set of VP2 position 300 mutants in infectious clones. Notably, some position 300 residues rendered CPV noninfectious for dog, but not cat or fox, cells. Changes of adjacent residues (residues 299 and 301) were also observed often after cell culture passage in different hosts, and some of the mutations mimicked changes seen in viruses recovered from natural infections of alternative hosts, suggesting that compensatory mutations were selected to accommodate the new residue at position 300. Analysis of the TfRs of carnivore hosts used in the experimental evolution studies demonstrated that their glycosylation patterns varied, including a glycan present only on the domestic dog TfR that dictates susceptibility to parvoviruses. Overall, there were significant differences in the abilities of viruses with alternative position 300 residues to bind TfRs and infect different carnivore hosts, demonstrating that the process of infection is highly host dependent and that VP2 position 300 is a key determinant of host range. IMPORTANCE Although the emergence and pandemic spread of canine parvovirus (CPV) are well documented, the carnivore hosts and evolutionary pathways involved in its emergence remain enigmatic. We recently demonstrated that a region in the capsid structure of CPV, centered around VP2 position 300

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

    Science.gov (United States)

    Salmasi, Mehrdad; Büttner, Ulrich; Glasauer, Stefan

    2016-06-01

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

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

    OpenAIRE

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

    2015-01-01

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

  1. Approach avoidance training in the eating domain: testing the effectiveness across three single session studies.

    Science.gov (United States)

    Becker, Daniela; Jostmann, Nils B; Wiers, Reinout W; Holland, Rob W

    2015-02-01

    Dual-process models propose that impulsive behavior plays a key role in the development and maintenance of maladaptive eating patterns. Research outside the eating domain suggests that approach avoidance training, a paradigm which aims to modify automatic behavioral dispositions toward critical stimuli, is an effective tool to weaken unhealthy impulses. The present research tested the effectiveness of approach avoidance training in the eating domain. We conducted three single session studies with varying methodologies in a normal-weight female student population (total N = 258), in which one group was always trained to avoid pictures of unhealthy food and to approach pictures of healthy food or neutral objects. We found no conclusive evidence that approach avoidance training can change participants' implicit and explicit food preferences and eating behavior. We discuss the potential and the limitations of approach avoidance training in the eating domain and provide suggestions for future research avenues. Copyright © 2014 Elsevier Ltd. All rights reserved.

  2. Dynamic response analysis of single-span guideway caused by high speed maglev train

    OpenAIRE

    Shi, Jin; Wang, Ying-Jie

    2011-01-01

    High speed maglev is one of the most important reformations in the ground transportation systems because of its no physical contact nature. This paper intends to study the dynamic response of the single-span guideway induced by moving maglev train. The dynamic model of the maglev train-guideway system is established. In this model, a maglev train consists of three vehicles and each vehicle is regarded as a multibody system with 34 degrees-of-freedom. The guideway is modeled as a simply suppor...

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

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

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

  4. Spike Neural Models Part II: Abstract Neural Models

    OpenAIRE

    Johnson, Melissa G.; Chartier, Sylvain

    2018-01-01

    Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN) though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF) model whic...

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

    Science.gov (United States)

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

    1999-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Nicholas V. Swindale

    2014-02-01

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

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

    Science.gov (United States)

    Swindale, Nicholas V; Spacek, Martin A

    2014-01-01

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

  8. Effect of speed endurance training and reduced training volume on running economy and single muscle fiber adaptations in trained runners

    DEFF Research Database (Denmark)

    Skovgaard, Casper; Christiansen, Danny; Christensen, Peter Møller

    2018-01-01

    The aim of the present study was to examine whether improved running economy with a period of speed endurance training and reduced training volume could be related to adaptations in specific muscle fibers. Twenty trained male (n = 14) and female (n = 6) runners (maximum oxygen consumption (VO2 -max...... was performed. In addition, running at 60% vVO2 -max, and a 10-km run was performed in a normal and a muscle slow twitch (ST) glycogen-depleted condition. After compared to before the intervention, expression of mitochondrial uncoupling protein 3 (UCP3) was lower (P ....05) in ST muscle fibers, and sarcoplasmic reticulum calcium ATPase 1 (SERCA1) was lower (P VO2 -max (11.6 ± 0.2 km/h) and at v10-km (13.7 ± 0.3 km/h) was ~2% better (P

  9. Training reduces catabolic and inflammatory response to a single practice in female volleyball players.

    Science.gov (United States)

    Eliakim, Alon; Portal, Shawn; Zadik, Zvi; Meckel, Yoav; Nemet, Dan

    2013-11-01

    We examined the effect of training on hormonal and inflammatory response to a single volleyball practice in elite adolescent players. Thirteen female, national team level, Israeli volleyball players (age 16.0 ± 1.4 years, Tanner stage 4-5) participated in the study. Blood samples were collected before and immediately after a typical 60 minutes of volleyball practice, before and after 7 weeks of training during the initial phase of the season. Training involved tactic and technical drills (20% of time), power and speed drills (25% of time), interval sessions (25% of time), endurance-type training (15% of time), and resistance training (15% of time). To achieve greater training responses, the study was performed during the early phase (first 7 weeks) of the volleyball season. Hormonal measurements included the anabolic hormones growth hormone (GH), insulin-like growth factor-I (IGF-I) and IGF-binding protein-3, the catabolic hormone cortisol, the proinflammatory marker interleukin-6 (IL-6), and the anti-inflammatory marker IL-1 receptor antagonist. Training led to a significant improvement of vertical jump, anaerobic properties (peak and mean power by the Wingate Anaerobic Test), and predicted VO2max (by the 20-m shuttle run). Volleyball practice, both before and after the training intervention, was associated with a significant increase of serum lactate, GH, and IL-6. Training resulted in a significantly reduced cortisol response ([INCREMENT]cortisol: 4.2 ± 13.7 vs. -4.4 ± 12.3 ng · ml, before and after training, respectively; p volleyball practice. The results suggest that along with the improvement of power and anaerobic and aerobic characteristics, training reduces the catabolic and inflammatory response to exercise.

  10. Synchronous spikes are necessary but not sufficient for a synchrony code in populations of spiking neurons.

    Science.gov (United States)

    Grewe, Jan; Kruscha, Alexandra; Lindner, Benjamin; Benda, Jan

    2017-03-07

    Synchronous activity in populations of neurons potentially encodes special stimulus features. Selective readout of either synchronous or asynchronous activity allows formation of two streams of information processing. Theoretical work predicts that such a synchrony code is a fundamental feature of populations of spiking neurons if they operate in specific noise and stimulus regimes. Here we experimentally test the theoretical predictions by quantifying and comparing neuronal response properties in tuberous and ampullary electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus These related systems show similar levels of synchronous activity, but only in the more irregularly firing tuberous afferents a synchrony code is established, whereas in the more regularly firing ampullary afferents it is not. The mere existence of synchronous activity is thus not sufficient for a synchrony code. Single-cell features such as the irregularity of spiking and the frequency dependence of the neuron's transfer function determine whether synchronous spikes possess a distinct meaning for the encoding of time-dependent signals.

  11. Effect of speed endurance training and reduced training volume on running economy and single muscle fiber adaptations in trained runners.

    Science.gov (United States)

    Skovgaard, Casper; Christiansen, Danny; Christensen, Peter M; Almquist, Nicki W; Thomassen, Martin; Bangsbo, Jens

    2018-02-01

    The aim of the present study was to examine whether improved running economy with a period of speed endurance training and reduced training volume could be related to adaptations in specific muscle fibers. Twenty trained male (n = 14) and female (n = 6) runners (maximum oxygen consumption (VO 2 -max): 56.4 ± 4.6 mL/min/kg) completed a 40-day intervention with 10 sessions of speed endurance training (5-10 × 30-sec maximal running) and a reduced (36%) volume of training. Before and after the intervention, a muscle biopsy was obtained at rest, and an incremental running test to exhaustion was performed. In addition, running at 60% vVO 2 -max, and a 10-km run was performed in a normal and a muscle slow twitch (ST) glycogen-depleted condition. After compared to before the intervention, expression of mitochondrial uncoupling protein 3 (UCP3) was lower (P < 0.05) and dystrophin was higher (P < 0.05) in ST muscle fibers, and sarcoplasmic reticulum calcium ATPase 1 (SERCA1) was lower (P < 0.05) in fast twitch muscle fibers. Running economy at 60% vVO 2 -max (11.6 ± 0.2 km/h) and at v10-km (13.7 ± 0.3 km/h) was ~2% better (P < 0.05) after the intervention in the normal condition, but unchanged in the ST glycogen-depleted condition. Ten kilometer performance was improved (P < 0.01) by 3.2% (43.7 ± 1.0 vs. 45.2 ± 1.2 min) and 3.9% (45.8 ± 1.2 vs. 47.7 ± 1.3 min) in the normal and the ST glycogen-depleted condition, respectively. VO 2 -max was the same, but vVO 2 -max was 2.0% higher (P < 0.05; 19.3 ± 0.3 vs. 18.9 ± 0.3 km/h) after than before the intervention. Thus, improved running economy with intense training may be related to changes in expression of proteins linked to energy consuming processes in primarily ST muscle fibers. © 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.

  12. Enhancing Cultural Adaptation through Friendship Training: A Single-Case Study.

    Science.gov (United States)

    Liu, Yi-Ching; Baker, Stanley B.

    1993-01-01

    Four-year-old girl from mainland China experienced culture shock when attending American university day-care center. Counseling intern from Taiwan designed friendship training program based on assumptions concerning adaptation, acculturation, and peer relationships. Evaluated as intensive single-case study, findings indicated the program may be…

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

    Science.gov (United States)

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

    2016-04-01

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

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

    Science.gov (United States)

    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.

  15. Effects of single vs. multiple-set short-term strength training in elderly women.

    Science.gov (United States)

    Radaelli, Regis; Wilhelm, Eurico N; Botton, Cíntia E; Rech, Anderson; Bottaro, Martim; Brown, Lee E; Pinto, Ronei S

    2014-01-01

    The strength training has been shown to be effective for attenuating the age-related physiological decline. However, the adequate volume of strength training volume adequate to promote improvements, mainly during the initial period of training, still remains controversial. Thus, the purpose of this study was to compare the effects of a short-term strength training program with single or multiple sets in elderly women. Maximal dynamic (1-RM) and isometric strength, muscle activation, muscle thickness (MT), and muscle quality (MQ = 1-RM and MT quadriceps quotient) of the knee extensors were assessed. Subjects were randomly assigned into one of two groups: single set (SS; n = 14) that performed one set per exercise or multiple sets (MS; n = 13) that performed three-sets per exercise, twice weekly for 6 weeks. Following training, there were significant increases (p ≤ 0.05) in knee extension 1-RM (16.1 ± 12 % for SS group and 21.7 ± 7.7 % for MS group), in all MT (p ≤ 0.05; vastus lateralis, rectus femoris, vastus medialis, and vastus intermedius), and in MQ (p ≤ 0.05); 15.0 ± 12.2 % for SS group and 12.6 ± 7.2 % for MS group), with no differences between groups. These results suggest that during the initial stages of strength training, single- and multiple-set training demonstrate similar capacity for increasing dynamic strength, MT, and MQ of the knee extensors in elderly women.

  16. Coronavirus spike-receptor interactions

    NARCIS (Netherlands)

    Mou, H.

    2015-01-01

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

  17. A Simple Deep Learning Method for Neuronal Spike Sorting

    Science.gov (United States)

    Yang, Kai; Wu, Haifeng; Zeng, Yu

    2017-10-01

    Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.

  18. Single and concurrent effects of endurance and resistance training on pulmonary function.

    Science.gov (United States)

    Khosravi, Maryam; Tayebi, Seyed Morteza; Safari, Hamed

    2013-04-01

    As not only few evidences but also contradictory results exist with regard to the effects of resistance training (RT) and resistance plus endurance training (ERT) on respiratory system, so the purpose of this research was therefore to study single and concurrent effects of endurance and resistance training on pulmonary function. Thirty seven volunteer healthy inactive women were randomly divided into 4 groups: without training as control (C), Endurance Training (ET), RT, and ERT. A spirometry test was taken 24 hrs before and after the training course. The training period (8 weeks, 3 sessions/week) for ET was 20-26 min/session running with 60-80% maximum heart rate (HR max); for RT two circuits/session, 40-60s for each exercise with 60-80% one repetition maximum (1RM), and 1 and 3 minutes active rest between exercises and circuits respectively; and for ERT was in agreement with either ET or RT protocols, but the times of running and circuits were half of ET and RT. ANCOVA showed that ET and ERT increased significantly (P0.05) on forced expiratory volume in one second (FEV1) and FEV1/FVC ratio. In conclusion, ET combined with RT (ERT) has greater effect on VC, FVC, FEF rating at25%-75%, and also on PEF except MVV, rather than RT, and just ET has greater effect rather than ERT.

  19. Virtual agent-mediated appraisal training: a single case series among Dutch firefighters.

    Science.gov (United States)

    Beer, Ursula M; Neerincx, Mark A; Morina, Nexhmedin; Brinkman, Willem-Paul

    2017-01-01

    Background : First responders are a prime example of professionals that are at a high risk of being exposed to traumatic experiences. Reappraisal as a coping strategy might help first responders to better cope with their emotional responses to traumatic events. Objective : This study investigated the effects of repeated sessions of a digital reappraisal training among seven firefighters. The training consisted of four sessions supported by a virtual agent, conducted at home or at work, over a two-week period in a single case series. Method : Sixteen data points were collected from each participant in the eight days pre- and post-training. Results : Significantly more themes were used at post-training than at pre-training, implying more flexibility and confirming the main hypothesis of the study. Negative side effects were not reported during or in the week after the training. Conclusions : More controlled studies into the short- and long-term effects of a training of this nature are needed. Furthermore, it provides a reference for developers in this field.

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

    Science.gov (United States)

    Malyshev, Aleksey; Stevenson, Ian H.

    2017-01-01

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

  1. On multi-site damage identification using single-site training data

    Science.gov (United States)

    Barthorpe, R. J.; Manson, G.; Worden, K.

    2017-11-01

    This paper proposes a methodology for developing multi-site damage location systems for engineering structures that can be trained using single-site damaged state data only. The methodology involves training a sequence of binary classifiers based upon single-site damage data and combining the developed classifiers into a robust multi-class damage locator. In this way, the multi-site damage identification problem may be decomposed into a sequence of binary decisions. In this paper Support Vector Classifiers are adopted as the means of making these binary decisions. The proposed methodology represents an advancement on the state of the art in the field of multi-site damage identification which require either: (1) full damaged state data from single- and multi-site damage cases or (2) the development of a physics-based model to make multi-site model predictions. The potential benefit of the proposed methodology is that a significantly reduced number of recorded damage states may be required in order to train a multi-site damage locator without recourse to physics-based model predictions. In this paper it is first demonstrated that Support Vector Classification represents an appropriate approach to the multi-site damage location problem, with methods for combining binary classifiers discussed. Next, the proposed methodology is demonstrated and evaluated through application to a real engineering structure - a Piper Tomahawk trainer aircraft wing - with its performance compared to classifiers trained using the full damaged-state dataset.

  2. Single- and multiple-set resistance training improves skeletal and respiratory muscle strength in elderly women.

    Science.gov (United States)

    Abrahin, Odilon; Rodrigues, Rejane P; Nascimento, Vanderson C; Da Silva-Grigoletto, Marzo E; Sousa, Evitom C; Marçal, Anderson C

    2014-01-01

    Aging involves a progressive reduction of respiratory muscle strength as well as muscle strength. Compare the effects of resistance training volume on the maximum inspiratory pressure (MIP), maximum expiratory pressure (MEP), functional performance, and muscle strength in elderly women. Thirty elderly women were randomly assigned to a group performing either single sets (1-SET) or three sets (3-SET) of exercises. The sit-to-stand test, MIP, MEP, and muscle strength were assessed before and after 24 training sessions. Progressive resistance training was performed two times per week for a total of 8-12 repetitions, using the main muscle groups of the upper and lower limbs. The main results showed that the participants significantly increased their MEP (Ptraining sessions, muscle strength also significantly increased (Ptraining programs increased MIP, MEP, muscle strength, and sit-to-stand test performance in elderly women after 24 sessions of training. In conclusion, our results suggested that elderly women who are not in the habit of physical activity may start with single-set resistance training programs as a short-term strategy for the maintenance of health.

  3. Comparison between Unilateral and Bilateral Plyometric Training on Single and Double Leg Jumping Performance and Strength.

    Science.gov (United States)

    Bogdanis, Gregory C; Tsoukos, Athanasios; Kaloheri, Olga; Terzis, Gerasimos; Veligekas, Panagiotis; Brown, Lee E

    2017-04-18

    This study compared the effects of unilateral and bilateral plyometric training on single and double-leg jumping performance, maximal strength and rate of force development (RFD). Fifteen moderately trained subjects were randomly assigned to either a unilateral (U, n=7) or bilateral group (B, n=8). Both groups performed maximal effort plyometric leg exercises two times per week for 6 weeks. The B group performed all exercises with both legs, while the U group performed half the repetitions with each leg, so that total exercise volume was the same. Jumping performance was assessed by countermovement jumps (CMJ) and drop jumps (DJ), while maximal isometric leg press strength and RFD were measured before and after training for each leg separately and both legs together. CMJ improvement with both legs was not significantly different between U (12.1±7.2%) and B (11.0±5.5%) groups. However, the sum of right and left leg CMJ only improved in the U group (19.0±7.1%, pplyometric training was more effective at increasing both single and double-leg jumping performance, isometric leg press maximal force and RFD when compared to bilateral training.

  4. Metacognitive training for patients with schizophrenia: preliminary evidence for a targeted, single-module programme.

    Science.gov (United States)

    Balzan, Ryan P; Delfabbro, Paul H; Galletly, Cherrie A; Woodward, Todd S

    2014-12-01

    Metacognitive training is an eight-module, group-based treatment programme for people with schizophrenia that targets the cognitive biases (i.e. problematic thinking styles) thought to contribute to the genesis and maintenance of delusions. The present article is an investigation into the efficacy of a shorter, more targeted, single-module metacognitive training programme, administered individually, which focuses specifically on improving cognitive biases that are thought to be driven by a 'hypersalience of evidence-hypothesis matches' mechanism (e.g. jumping to conclusions, belief inflexibility, reasoning heuristics, illusions of control). It was hypothesised that a more targeted metacognitive training module could still improve performance on these bias tasks and reduce delusional ideation, while improving insight and quality of life. A sample of 28 patients diagnosed with schizophrenia and mild delusions either participated in the hour-long, single-session, targeted metacognitive training programme (n = 14), or continued treatment as usual (n = 14). All patients were assessed using clinical measures gauging overall positive symptomology, delusional ideation, quality of life and insight, and completed two cognitive bias tasks designed to elucidate the representativeness and illusion of control biases. After a 2-week, post-treatment interval, targeted metacognitive training patients exhibited significant decreases in delusional severity and conviction, significantly improved clinical insight, and significant improvements on the cognitive bias tasks, relative to the treatment-as-usual controls. Performance improvements on the cognitive bias tasks significantly correlated with the observed reductions in overall positive symptomology. Patients also evaluated the training positively. Although interpretations of these results are limited due to the lack of an optimally designed, randomised controlled trial and a small sample size, the results are promising and warrant

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

    Directory of Open Access Journals (Sweden)

    Zedong eBi

    2016-02-01

    Full Text Available In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons and synapses as well as the randomness of connection details, spike trains typically exhibit variability such as spatial randomness and temporal stochasticity, resulting in variability of synaptic changes under plasticity, which we call efficacy variability. How the variability of spike trains influences the efficacy variability of synapses remains unclear. In this paper, we try to understand this influence under pair-wise additive spike-timing dependent plasticity (STDP when the mean strength of plastic synapses into a neuron is bounded (synaptic homeostasis. Specifically, we systematically study, analytically and numerically, how four aspects of statistical features, i.e. synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations, as well as their interactions influence the efficacy variability in converging motifs (simple networks in which one neuron receives from many other neurons. Neurons (including the post-synaptic neuron in a converging motif generate spikes according to statistical models with tunable parameters. In this way, we can explicitly control the statistics of the spike patterns, and investigate their influence onto the efficacy variability, without worrying about the feedback from synaptic changes onto the dynamics of the post-synaptic neuron. We separate efficacy variability into two parts: the drift part (DriftV induced by the heterogeneity of change rates of different synapses, and the diffusion part (DiffV induced by weight diffusion caused by stochasticity of spike trains. Our main findings are: (1 synchronous firing and burstiness tend to increase DiffV, (2 heterogeneity of rates induces DriftV when potentiation and depression in STDP are not balanced, and (3 heterogeneity of cross-correlations induces DriftV together with heterogeneity of rates. We anticipate our

  6. Effects of arm training with the robotic device ARMin I in chronic stroke: three single cases.

    Science.gov (United States)

    Nef, Tobias; Quinter, Gabriela; Müller, Roland; Riener, Robert

    2009-01-01

    Several clinical studies on chronic stroke conducted with end-effector-based robots showed improvement of the motor function in the affected arm. Compared to end-effector-based robots, exoskeleton robots provide improved guidance of the human limb and are better suited to train task-oriented movements with a large range of motions. To test whether intensive arm training with the arm exoskeleton ARMin I is feasible with chronic-stroke patients and whether it improves motor function in the paretic arm. Three single cases with chronic hemiparesis resulting from unilateral stroke (at least 14 months after stroke). A-B design with 2 weeks of multiple baseline measurements (A), 8 weeks of training (B) with repetitive measurements and a follow-up measurement 8 weeks after training. The training included shoulder and elbow movements with the robotic rehabilitation device ARMin I. Two subjects had three 1-hour sessions per week and 1 subject received five 1-hour sessions per week. The main outcome measurement was the upper-limb part of the Fugl-Meyer Assessment (FMA). The ARMin training was well tolerated by the patients, and the FMA showed moderate, but significant improvements for all 3 subjects (p arm exoskeleton is feasible with chronic-stroke patients. Moderate improvements were found in all 3 subjects, thus further clinical investigations are justified. Copyright 2009 S. Karger AG, Basel.

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

    Science.gov (United States)

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

    2015-07-01

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

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

    Science.gov (United States)

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

    2015-02-01

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

  9. Wavelet analysis of epileptic spikes

    CERN Document Server

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

    2003-01-01

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

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

    OpenAIRE

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

    2012-01-01

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

  11. The effects of assertiveness training in patients with schizophrenia: a randomized, single-blind, controlled study.

    Science.gov (United States)

    Lee, Tso-Ying; Chang, Shih-Chin; Chu, Hsin; Yang, Chyn-Yng; Ou, Keng-Liang; Chung, Min-Huey; Chou, Kuei-Ru

    2013-11-01

    In this study, we investigated the effects of group assertiveness training on assertiveness, social anxiety and satisfaction with interpersonal communication among patients with chronic schizophrenia. Only limited studies highlighted the effectiveness of group assertiveness training among inpatients with schizophrenia. Given the lack of group assertiveness training among patients with schizophrenia, further development of programmes focusing on facilitating assertiveness, self-confidence and social skills among inpatients with chronic schizophrenia is needed. This study used a prospective, randomized, single-blinded, parallel-group design. This study employed a prospective, randomized, parallel-group design. Seventy-four patients were randomly assigned to experimental group receiving 12 sessions of assertiveness training, or a supportive control group. Data collection took place for the period of June 2009-July 2010. Among patients with chronic schizophrenia, assertiveness, levels of social anxiety and satisfaction with interpersonal communication significantly improved immediately after the intervention and at the 3-month follow-up in the intervention group. The results of a generalized estimating equation (GEE) indicated that: (1) assertiveness significantly improved from pre- to postintervention and was maintained until the follow-up; (2) anxiety regarding social interactions significantly decreased after assertiveness training; and (3) satisfaction with interpersonal communication slightly improved after the 12-session intervention and at the 3-month follow-up. Assertivenss training is a non-invasive and inexpensive therapy that appears to improve assertiveness, social anxiety and interpersonal communication among inpatients with chronic schizophrenia. These findings may provide a reference guide to clinical nurses for developing assertiveness-training protocols. © 2013 Blackwell Publishing Ltd.

  12. Skill transfer from symmetric and asymmetric bimanual training using a robotic system to single limb performance.

    Science.gov (United States)

    Trlep, Matic; Mihelj, Matjaž; Munih, Marko

    2012-07-17

    . Transfer of learned skills from bimanual training to unimanual movements was also observed, as bimanual training also improved single limb performance with the dominant arm. Changes of force symmetry did not have an effect on motor learning. As motor learning is believed to be an important mechanism of rehabilitation, our findings could be tested for future post-stroke rehabilitation systems.

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

    Directory of Open Access Journals (Sweden)

    Hamid Emadi-Koochak

    2016-05-01

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

  14. Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task.

    Directory of Open Access Journals (Sweden)

    Pavel Sanda

    2017-09-01

    Full Text Available Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making.

  15. Acute-Phase Inflammatory Response to Single-Bout HIIT and Endurance Training: A Comparative Study.

    Science.gov (United States)

    Kaspar, Felix; Jelinek, Herbert F; Perkins, Steven; Al-Aubaidy, Hayder A; deJong, Bev; Butkowski, Eugene

    2016-01-01

    This study compared acute and late effect of single-bout endurance training (ET) and high-intensity interval training (HIIT) on the plasma levels of four inflammatory cytokines and C-reactive protein and insulin-like growth factor 1. Cohort study with repeated-measures design. Seven healthy untrained volunteers completed a single bout of ET and HIIT on a cycle ergometer. ET and HIIT sessions were held in random order and at least 7 days apart. Blood was drawn before the interventions and 30 min and 2 days after the training sessions. Plasma samples were analyzed with ELISA for the interleukins (IL), IL-1β, IL-6, and IL-10, monocyte chemoattractant protein-1 (MCP-1), insulin growth factor 1 (IGF-1), and C-reactive protein (CRP). Statistical analysis was with Wilcoxon signed-rank tests. ET led to both a significant acute and long-term inflammatory response with a significant decrease at 30 minutes after exercise in the IL-6/IL-10 ratio (-20%; p = 0.047) and a decrease of MCP-1 (-17.9%; p = 0.03). This study demonstrates that ET affects the inflammatory response more adversely at 30 minutes after exercise compared to HIIT. However, this is compensated by a significant decrease in MCP-1 at two days associated with a reduced risk of atherosclerosis.

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

    Science.gov (United States)

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

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    George L Chadderdon

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

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

    Directory of Open Access Journals (Sweden)

    Douglas L Jones

    2015-06-01

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

  19. Learning Curve for Laparoendoscopic Single-Incision Live Donor Nephrectomy: Implications for Laparoendoscopic Practice and Training.

    Science.gov (United States)

    Troppmann, Christoph; Santhanakrishnan, Chandrasekar; Fananapazir, Ghaneh; Troppmann, Kathrin M; Perez, Richard V

    2017-05-01

    The learning curve for laparoendoscopic single-incision live donor nephrectomy, which is technically more complex than the multiport, conventional laparoendoscopic approach, is unknown. In a retrospective cohort study, we analyzed the learning curve of the initial 114 consecutive single-incision laparoendoscopic nephrectomies performed in nonselected live kidney donors. Median donor body mass index was 26 kg/m 2 (range 20-34). In all, 92% of the nephrectomies were performed on the left side; 18% of the recovered kidneys had multiple renal arteries. Cumulative sum (CUSUM) analysis of operating time (OT) demonstrated that the learning curve was achieved after case 61. For the learning curve phase (Group 1 [cases 1-61]) vs the postlearning phase (Group 2 [cases 62-114]), the difference of the mean OT was 20 minutes (p = 0.05). Mean warm ischemic time in the donors was significantly longer during the learning phase (Group 1, 6 minutes; Group 2, 5 minutes; p = 0.04). Rates of conversions to multiport procedures and of donor complications were not significantly different between Groups 1 and 2. For the recipients, we observed delayed graft function in 2 (2%) cases, no technical graft losses; and 1-year death-censored graft survival was 100% (p = n.s. for all comparisons of Group 1 vs 2). Single-incision laparoendoscopic donor nephrectomy had a long learning curve (>60 cases), but resulted in excellent donor and recipient outcomes. The long learning curve has significant implications for the programs and surgeons who contemplate transitioning from multiport to single-incision nephrectomy. Furthermore, our observations are highly relevant for informing the development of training requirements for fellows to be trained in single-incision laparoendoscopic nephrectomy.

  20. Absolute Ca Isotopic Measurement Using an Improved Double Spike Technique

    Directory of Open Access Journals (Sweden)

    Jason Jiun-San Shen

    2009-01-01

    Full Text Available A new vector analytical method has been developed in order to obtain the true isotopic composition of the 42Ca-48Ca double spike. This is achieved by using two different sample-spike mixtures combined with the double spike and natural Ca data. Be cause the natural sample (two mixtures and the spike should all lie on a single mixing line, we are able to con strain the true isotopic composition of our double spike using this new approach. Once the isotopic composition of the Ca double spike is established, we are able to obtain the true Ca isotopic composition of the NIST Ca standard SRM915a, 40Ca/44Ca = 46.537 ± 2 (2sm, n = 55, 42Ca/44Ca = 0.31031 ± 1, 43Ca/44Ca = 0.06474 ± 1, and 48Ca/44Ca = 0.08956 ± 1. De spite an off set of 1.3% in 40Ca/44Ca between our result and the previously re ported value (Russell et al. 1978, our data indicate an off set of 1.89__in 40Ca/44Ca between SRM915a and seawater, entirely consistent with the published results.

  1. Spiking the expectancy profiles

    DEFF Research Database (Denmark)

    Hansen, Niels Chr.; Loui, Psyche; Vuust, Peter

    Melodic expectations have long been quantified using expectedness ratings. Motivated by statistical learning and sharper key profiles in musicians, we model musical learning as a process of reducing the relative entropy between listeners' prior expectancy profiles and probability distributions...... of a given musical style or of stimuli used in short-term experiments. Five previous probe-tone experiments with musicians and non-musicians are revisited. Exp. 1-2 used jazz, classical and hymn melodies. Exp. 3-5 collected ratings before and after exposure to 5, 15 or 400 novel melodies generated from...... a finite-state grammar using the Bohlen-Pierce scale. We find group differences in entropy corresponding to degree and relevance of musical training and within-participant decreases after short-term exposure. Thus, whereas inexperienced listeners make high-entropy predictions by default, statistical...

  2. Spike propagation through the dorsal root ganglia in an unmyelinated sensory neuron: a modeling study.

    Science.gov (United States)

    Sundt, Danielle; Gamper, Nikita; Jaffe, David B

    2015-12-01

    Unmyelinated C-fibers are a major type of sensory neurons conveying pain information. Action potential conduction is regulated by the bifurcation (T-junction) of sensory neuron axons within the dorsal root ganglia (DRG). Understanding how C-fiber signaling is influenced by the morphology of the T-junction and the local expression of ion channels is important for understanding pain signaling. In this study we used biophysical computer modeling to investigate the influence of axon morphology within the DRG and various membrane conductances on the reliability of spike propagation. As expected, calculated input impedance and the amplitude of propagating action potentials were both lowest at the T-junction. Propagation reliability for single spikes was highly sensitive to the diameter of the stem axon and the density of voltage-gated Na(+) channels. A model containing only fast voltage-gated Na(+) and delayed-rectifier K(+) channels conducted trains of spikes up to frequencies of 110 Hz. The addition of slowly activating KCNQ channels (i.e., KV7 or M-channels) to the model reduced the following frequency to 30 Hz. Hyperpolarization produced by addition of a much slower conductance, such as a Ca(2+)-dependent K(+) current, was needed to reduce the following frequency to 6 Hz. Attenuation of driving force due to ion accumulation or hyperpolarization produced by a Na(+)-K(+) pump had no effect on following frequency but could influence the reliability of spike propagation mutually with the voltage shift generated by a Ca(2+)-dependent K(+) current. These simulations suggest how specific ion channels within the DRG may contribute toward therapeutic treatments for chronic pain. Copyright © 2015 the American Physiological Society.

  3. An online supervised learning method based on gradient descent for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Yang, Jing; Zhong, Shuiming

    2017-09-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2013-01-01

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

  5. Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems

    Directory of Open Access Journals (Sweden)

    Emre eNeftci

    2014-01-01

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

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

    Science.gov (United States)

    Balachandar, Arjun; Prescott, Steven A

    2018-01-20

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

  7. Is modified brief assertiveness training for nurses effective? A single-group study with long-term follow-up.

    Science.gov (United States)

    Yoshinaga, Naoki; Nakamura, Yohei; Tanoue, Hiroki; MacLiam, Fionnula; Aoishi, Keiko; Shiraishi, Yuko

    2018-01-01

    To evaluate the long-term effectiveness of modified brief assertiveness training (with cognitive techniques) for nurses. Most assertiveness training takes a long time to conduct; thus, briefer training is required for universal on-the-job training in the workplace. In this single-group study, nurses received two 90-min training sessions with a 1-month interval between sessions. The degree of assertiveness was assessed by using the Rathus Assertiveness Schedule as the primary outcome, at four time points: pre- and post-training, 3-month follow-up and 6-month follow-up. A total of 33 nurses received the training, and the mean Rathus Assertiveness Schedule score improved from -14.2 (SD = 16.5) pre-training to -10.5 (SD = 18.0) post-training (p training. Modified brief assertiveness training seems feasible and may achieve long-term favourable outcomes in improving assertiveness among nurses. The ease of implementation of assertiveness training is important because creating an open environment for communication leads to improved job satisfaction, improved nursing care and increased patient safety. © 2017 The Authors. Journal of Nursing Management Published by John Wiley & Sons Ltd.

  8. Spike Pattern Recognition for Automatic Collimation Alignment

    CERN Document Server

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

    2017-01-01

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

  9. Spiking Neural Network in Precision Agriculture

    Directory of Open Access Journals (Sweden)

    Nadia Adnan Shiltagh

    2015-07-01

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

  10. Interactions between procedural learning and cocaine exposure alter spontaneous and cortically-evoked spike activity in the dorsal striatum

    Directory of Open Access Journals (Sweden)

    Janie eOndracek

    2010-12-01

    Full Text Available We have previously shown that cocaine enhances gene regulation in the sensorimotor striatum associated with procedural learning in a running-wheel paradigm. Here we assessed whether cocaine produces enduring modifications of learning-related changes in striatal neuron activity, using single-unit recordings in anesthetized rats 1 day after the wheel training. Spontaneous and cortically-evoked spike activity was compared between groups treated with cocaine or vehicle immediately prior to the running-wheel training or placement in a locked wheel (control conditions. We found that wheel training in vehicle-treated rats increased the average firing rate of spontaneously active neurons without changing the relative proportion of active to quiescent cells. In contrast, in rats trained under the influence of cocaine, the proportion of spontaneously firing to quiescent cells was significantly greater than in vehicle-treated, trained rats. However, this effect was associated with a lower average firing rate in these spontaneously active cells, suggesting that training under the influence of cocaine recruited additional low-firing cells. Measures of cortically-evoked activity revealed a second interaction between cocaine treatment and wheel training, namely, a cocaine-induced decrease in spike onset latency in control rats (locked wheel. This facilitatory effect of cocaine was abolished when rats trained in the running wheel during cocaine action. These findings highlight important interactions between cocaine and procedural learning, which act to modify population firing activity and the responsiveness of striatal neurons to excitatory inputs. Moreover, these effects were found 24 hours after the training and last drug exposure indicating that cocaine exposure during the learning phase triggers long-lasting changes in synaptic plasticity in the dorsal striatum. Such changes may contribute to the transition from recreational to habitual or compulsive drug

  11. Irisin in blood increases transiently after single sessions of intense endurance exercise and heavy strength training.

    Science.gov (United States)

    Nygaard, Håvard; Slettaløkken, Gunnar; Vegge, Geir; Hollan, Ivana; Whist, Jon Elling; Strand, Tor; Rønnestad, Bent R; Ellefsen, Stian

    2015-01-01

    Irisin is a recently identified exercise-induced hormone that increases energy expenditure, at least in rodents. The main purpose of this study was to test the hypothesis that Irisin increases acutely in blood after singular sessions of intense endurance exercise (END) and heavy strength training (STR). Secondary, we wanted to explore the relationship between body composition and exercise-induced effects on irisin, and the effect of END and STR on muscular expression of the irisin gene FNDC5. Nine moderately trained healthy subjects performed three test days using a randomized and standardized crossover design: one day with 60 minutes of END, one day with 60 minutes of STR, and one day without exercise (CON). Venous blood was sampled over a period of 24h on the exercise days. Both END and STR led to transient increases in irisin concentrations in blood, peaking immediately after END and one hour after STR, before gradually returning to baseline. Irisin responses to STR, but not END, showed a consistently strong negative correlation with proportions of lean body mass. Neither END nor STR affected expression of FNDC5, measured 4h after training sessions, though both protocols led to pronounced increases in PGC-1α expression, which is involved in transcriptional control of FNDC5. The results strongly suggest that single sessions of intense endurance exercise and heavy strength training lead to transient increases in irisin concentrations in blood. This was not accompanied by increased FNDC5 expression, measured 4h post-exercise. The results suggest that irisin responses to resistance exercise are higher in individuals with lower proportions of lean body mass.

  12. A matched-filter algorithm to detect amperometric spikes resulting from quantal secretion.

    Science.gov (United States)

    Balaji Ramachandran, Supriya; Gillis, Kevin D

    2018-01-01

    Electrochemical microelectrodes located immediately adjacent to the cell surface can detect spikes of amperometric current during exocytosis as the transmitter released from a single vesicle is oxidized on the electrode surface. Automated techniques to detect spikes are needed in order to quantify the spike rate as a measure of the rate of exocytosis. We have developed a Matched Filter (MF) detection algorithm that scans the data set with a library of prototype spike templates while performing a least-squares fit to determine the amplitude and standard error. The ratio of the fit amplitude to the standard error constitutes a criterion score that is assigned for each time point and for each template. A spike is detected when the criterion score exceeds a threshold and the highest-scoring template and the time of peak score is identified. The search for the next spike commences only after the score falls below a second, lower threshold to reduce false positives. The approach was extended to detect spikes with double-exponential decays with the sum of two templates. Receiver Operating Characteristic plots (ROCs) demonstrate that the algorithm detects >95% of manually identified spikes with a false-positive rate of ∼2%. ROCs demonstrate that the MF algorithm performs better than algorithms that detect spikes based on a derivative-threshold approach. The MF approach performs well and leads into approaches to identify spike parameters. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality

    Directory of Open Access Journals (Sweden)

    James P Crutchfield

    2015-08-01

    Full Text Available 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 towards that larger goal is todevelop information measures for individual output processes, including information generation (entropy rate, stored information (statisticalcomplexity, 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., $tau$-entropy rates that diverge less quickly than the firing rate indicate 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.

  14. What the training of a neuronal network optimizes.

    Science.gov (United States)

    Tabor, Zbisław

    2007-09-01

    In the study a model of training of neuronal networks built of integrate-and-fire neurons is investigated. Neurons are assembled into complex networks of Watts-Strogatz type. Every neuronal network contains a single receptor neuron. The receptor neuron, stimulated by an external signal, evokes spikes in equal time intervals. The spikes generated by the receptor neuron induce subsequent activity of a whole network. The depolarization signals, traveling the network, modify synaptic couplings according to a kick-and-delay rule, whose process is termed "training." It is shown that the training decreases the mean length of paths along which a depolarization signal is transmitted from the receptor neuron. Consequently, the training also decreases the reaction time and the energy expense necessary for the network to react to the external stimulus. It is shown that the initial distribution of synaptic couplings crucially determines the performance of trained networks.

  15. Long term, stable brain machine interface performance using local field potentials and multiunit spikes

    Science.gov (United States)

    Flint, Robert D.; Wright, Zachary A.; Scheid, Michael R.; Slutzky, Marc W.

    2013-10-01

    Objective. Brain machine interfaces (BMIs) have the potential to restore movement to people with paralysis. However, a clinically-viable BMI must enable consistently accurate control over time spans ranging from years to decades, which has not yet been demonstrated. Most BMIs that use single-unit spikes as inputs will experience degraded performance over time without frequent decoder re-training. Two other signals, local field potentials (LFPs) and multi-unit spikes (MSPs), may offer greater reliability over long periods and better performance stability than single-unit spikes. Here, we demonstrate that LFPs can be used in a biomimetic BMI to control a computer cursor. Approach. We implanted two rhesus macaques with intracortical microelectrodes in primary motor cortex. We recorded LFP and MSP signals from the monkeys while they performed a continuous reaching task, moving a cursor to randomly-placed targets on a computer screen. We then used the LFP and MSP signals to construct biomimetic decoders for control of the cursor. Main results. Both monkeys achieved high-performance, continuous control that remained stable or improved over nearly 12 months using an LFP decoder that was not retrained or adapted. In parallel, the monkeys used MSPs to control a BMI without retraining or adaptation and had similar or better performance, and that predominantly remained stable over more than six months. In contrast to their stable online control, both LFP and MSP signals showed substantial variability when used offline to predict hand movements. Significance. Our results suggest that the monkeys were able to stabilize the relationship between neural activity and cursor movement during online BMI control, despite variability in the relationship between neural activity and hand movements.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

    Florian, Răzvan V.

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Răzvan V Florian

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

  19. Effect of intermittent hypoxic training on hypoxia tolerance based on single-channel EEG.

    Science.gov (United States)

    Zhang, Tinglin; Wang, You; Li, Guang

    2016-03-23

    A single-channel algorithm was proposed in order to study effect of intermittent hypoxic training on hypoxia tolerance based on EEG pattern. EEG was decomposed by ensemble empirical mode decomposition into a finite number of intrinsic mode functions (IMFs) based on the intrinsic local characteristic time scale. Analytic amplitude, analytic frequency, and recurrence property quantified by recurrence quantification analysis were explored on IMFs, and the first two scales revealed difference between normal EEG and hypoxia EEG. Classification accuracy of hypoxia EEG and normal EEG could reach 67.8% before decline of neurobehavioral ability, which represented that hypoxia EEG pattern could be detected at an early stage. Classification accuracy of hypoxia EEG and normal EEG increased with time and deepened intensity of hypoxia was observed by regular shift of hypoxia EEG pattern with time in a three dimensional subspace. The reduced shift and classification accuracy after intermittent hypoxic training represented that hypoxia tolerance enhanced. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

    International Nuclear Information System (INIS)

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

    2012-01-01

    This paper presents iSpike: a C++ library that interfaces between spiking neural network simulators and the iCub humanoid robot. It uses a biologically inspired approach to convert the robot’s sensory information into spikes that are passed to the neural network simulator, and it decodes output spikes from the network into motor signals that are sent to control the robot. Applications of iSpike range from embodied models of the brain to the development of intelligent robots using biologically inspired spiking neural networks. iSpike is an open source library that is available for free download under the terms of the GPL. (paper)

  1. One night of partial sleep deprivation impairs recovery from a single exercise training session.

    Science.gov (United States)

    Rae, Dale E; Chin, Tayla; Dikgomo, Kagiso; Hill, Lee; McKune, Andrew J; Kohn, Tertius A; Roden, Laura C

    2017-04-01

    The effects of sleep deprivation on physical performance are well documented, but data on the consequence of sleep deprivation on recovery from exercise are limited. The aim was to compare cyclists' recovery from a single bout of high-intensity interval training (HIIT) after which they were given either a normal night of sleep (CON, 7.56 ± 0.63 h) or half of their usual time in bed (DEP, 3.83 ± 0.33 h). In this randomized cross-over intervention study, 16 trained male cyclists (age 32 ± 7 years), relative peak power output (PPO 4.6 ± 0.7 W kg -1 ) performed a HIIT session at ±18:00 followed by either the CON or DEP sleep condition. Recovery from the HIIT session was assessed the following day by comparing pre-HIIT variables to those measured 12 and 24 h after the session. Following a 2-week washout, cyclists repeated the trial, but under the alternate sleep condition. PPO was reduced more 24 h after the HIIT session in the DEP (ΔPPO -0.22 ± 0.22 W kg -1 ; range -0.75 to 0.1 W kg -1 ) compared to the CON condition (ΔPPO -0.05 ± 0.09 W kg -1 , range -0.19 to 0.17 W kg -1 , p = 0.008, d = -2.16). Cyclists were sleepier (12 h: p = 0.002, d = 1.90; 24 h: p = 0.001, d = 1.41) and felt less motivated to train (12 h, p = 0.012, d = -0.89) during the 24 h recovery phase when the HIIT session was followed by the DEP condition. The exercise-induced 24 h reduction in systolic blood pressure observed in the CON condition was absent in the DEP condition (p = 0.039, d = 0.75). One night of partial sleep deprivation impairs recovery from a single HIIT session in cyclists. Further research is needed to understand the mechanisms behind this observation.

  2. Ex-vivo training model for laparoendoscopic single-site surgery

    Directory of Open Access Journals (Sweden)

    Kommu Sashi

    2011-01-01

    Full Text Available Background: Laparoendoscopic single-site surgery (LESS has recently been applied successfully in the performance of a host of surgical procedures. Preliminary consensus from the experts is that this mode of surgery is technically challenging and requires expertise. The transition from trainee to practicing surgeon, especially in complex procedures with challenging learning curves, takes time and mentor-guided nurturing. However, the trainee needs to use platforms of training to gain the skills that are deemed necessary for undertaking the live human case. Objective: This article aims to demonstrate a step-by-step means of how to acquire the necessary instrumentation and build a training model for practicing steeplechase exercises in LESS for urological surgeons and trainees. The tool built as a result of this could set the platform for performance of basic and advanced skills uptake using conventional, bent and articulated instruments. A preliminary construct validity of the platform was conducted. Materials and Methods: A box model was fitted with an R-Port™ and camera. Articulated and conventional instruments were used to demonstrate basic exercises (e.g. glove pattern cutting, loop stacking and suturing and advanced exercises (e.g. pyeloplasty. The validation included medical students (M, final year laparoscopic fellows (F and experienced consultant laparoscopic surgeons (C with at least 50 LESS cases experience in total, were tested on eight basic skill tasks (S including manipulation of the flexible cystoscope (S1, hand eye coordination (S2, cutting with flexible scissors (S3, grasping with flexible needle holders (S4, two-handed maneuvers (S5, object translocation (S6, cross hand suturing with flexible instruments (S7 and conduction of an ex-vivo pyeloplasty. Results: The successful application of the box model was demonstrated by trainee based exercises. The cost of the kit with circulated materials was less than £150 (Pounds Sterling

  3. The effects of a single bout of exercise on motor memory interference in the trained and untrained hemisphere

    OpenAIRE

    Lauber, Benedikt; Franke, Steffen; Taube, Wolfgang; Gollhofer, Albert

    2017-01-01

    Increasing evidence suggests that cardiovascular exercise has positive effects on motor memory consolidation. In this study, we investigated whether a single session of high-intensity interval training (HIIT) mitigates the effects of practicing an interfering motor task. Furthermore, learning and interference effects were assessed in the actively trained and untrained limb as it is known that unilateral motor learning can cause bilateral adaptations.Subjects performed a ballistic trainin...

  4. Feature Representations for Neuromorphic Audio Spike Streams.

    Science.gov (United States)

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

    2018-01-01

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

  5. The effects of a single bout of exercise on motor memory interference in the trained and untrained hemisphere.

    Science.gov (United States)

    Lauber, Benedikt; Franke, Steffen; Taube, Wolfgang; Gollhofer, Albert

    2017-04-07

    Increasing evidence suggests that cardiovascular exercise has positive effects on motor memory consolidation. In this study, we investigated whether a single session of high-intensity interval training (HIIT) mitigates the effects of practicing an interfering motor task. Furthermore, learning and interference effects were assessed in the actively trained and untrained limb as it is known that unilateral motor learning can cause bilateral adaptations. Subjects performed a ballistic training and then the HIIT either before (HIIT_before) or after (HIIT_after) practicing an interfering accuracy task (AT). The control group (No_HIIT) did not participate in the HIIT but rested instead. Performance in the ballistic task (BT) was tested before and after the ballistic training, after the exercise and practice of the AT and 24h later. After ballistic training, all groups showed comparable increases in performance in the trained and untrained limb. Despite the practice of the AT, HIIT_before maintained their BT performance after the high-intensity interval training whereas HIIT_after (trend) & No_HIIT showed prominent interference effects. After 24h, HIIT_before still did not show any interference effects but further improved ballistic motor performance. HIIT_after counteracted the interference resulting in a comparable BT performance after 24h than directly after the ballistic training while No_HIIT had a significantly lower BT performance in the retention test. The results were similar in the trained and untrained limb. The current results imply that a single session of cardiovascular exercise can prevent motor interference in the trained and untrained hemisphere. Overall learning was best, and interference least, when HIIT was performed before the interfering motor task. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Saket Kumar Choudhary

    2016-06-01

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

  7. Cochlear spike synchronization and neuron coincidence detection model

    Science.gov (United States)

    Bader, Rolf

    2018-02-01

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

  8. Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

    DEFF Research Database (Denmark)

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

    2016-01-01

    by evoked spiking. This study of laminar recordings of spontaneous spiking and visually evoked spiking of neurons in the ferret primary visual cortex shows that the spiking dynamics does not change: the spontaneous spiking as well as evoked spiking is controlled by a stable and persisting fixed point...

  9. Force sensor in simulated skin and neural model mimic tactile SAI afferent spiking response to ramp and hold stimuli.

    Science.gov (United States)

    Kim, Elmer K; Wellnitz, Scott A; Bourdon, Sarah M; Lumpkin, Ellen A; Gerling, Gregory J

    2012-07-23

    The next generation of prosthetic limbs will restore sensory feedback to the nervous system by mimicking how skin mechanoreceptors, innervated by afferents, produce trains of action potentials in response to compressive stimuli. Prior work has addressed building sensors within skin substitutes for robotics, modeling skin mechanics and neural dynamics of mechanotransduction, and predicting response timing of action potentials for vibration. The effort here is unique because it accounts for skin elasticity by measuring force within simulated skin, utilizes few free model parameters for parsimony, and separates parameter fitting and model validation. Additionally, the ramp-and-hold, sustained stimuli used in this work capture the essential features of the everyday task of contacting and holding an object. This systems integration effort computationally replicates the neural firing behavior for a slowly adapting type I (SAI) afferent in its temporally varying response to both intensity and rate of indentation force by combining a physical force sensor, housed in a skin-like substrate, with a mathematical model of neuronal spiking, the leaky integrate-and-fire. Comparison experiments were then conducted using ramp-and-hold stimuli on both the spiking-sensor model and mouse SAI afferents. The model parameters were iteratively fit against recorded SAI interspike intervals (ISI) before validating the model to assess its performance. Model-predicted spike firing compares favorably with that observed for single SAI afferents. As indentation magnitude increases (1.2, 1.3, to 1.4 mm), mean ISI decreases from 98.81 ± 24.73, 54.52 ± 6.94, to 41.11 ± 6.11 ms. Moreover, as rate of ramp-up increases, ISI during ramp-up decreases from 21.85 ± 5.33, 19.98 ± 3.10, to 15.42 ± 2.41 ms. Considering first spikes, the predicted latencies exhibited a decreasing trend as stimulus rate increased, as is observed in afferent recordings. Finally, the SAI afferent's characteristic response

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

    Directory of Open Access Journals (Sweden)

    Christian Albers

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

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

    Science.gov (United States)

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

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

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

    Science.gov (United States)

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

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

  13. A memristive spiking neuron with firing rate coding

    Directory of Open Access Journals (Sweden)

    Marina eIgnatov

    2015-10-01

    Full Text Available Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2 and on the chemical electromigration cell Ag/TiO2-x/Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.

  14. A memristive spiking neuron with firing rate coding.

    Science.gov (United States)

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

    2015-01-01

    Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2) and on the chemical electromigration cell Ag/TiO2-x /Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2014-06-01

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

  17. Whole body, long-axis rotational training improves lower extremity neuromuscular control during single leg lateral drop landing and stabilization.

    Science.gov (United States)

    Nyland, John; Burden, Robert; Krupp, Ryan; Caborn, David N M

    2011-05-01

    Poor neuromuscular control during sports activities is associated with non-contact lower extremity injuries. This study evaluated the efficacy of progressive resistance, whole body, long-axis rotational training to improve lower extremity neuromuscular control during a single leg lateral drop landing and stabilization. Thirty-six healthy subjects were randomly assigned to either Training or Control groups. Electromyographic, ground reaction force, and kinematic data were collected from three pre-test, post-test trials. Independent sample t-tests with Bonferroni corrections for multiple comparisons were used to compare group mean change differences (P≤0.05/21≤0.0023). Training group gluteus maximus and gluteus medius neuromuscular efficiency improved 35.7% and 31.7%, respectively. Training group composite vertical-anteroposterior-mediolateral ground reaction force stabilization timing occurred 1.35s earlier. Training group knee flexion angle at landing increased by 3.5°. Training group time period between the initial two peak frontal plane knee displacements following landing increased by 0.17s. Training group peak hip and knee flexion velocity were 21.2°/s and 20.1°/s slower, respectively. Time period between the initial two peak frontal plane knee displacements following landing and peak hip flexion velocity mean change differences displayed a strong relationship in the Training group (r(2)=0.77, P=0.0001) suggesting improved dynamic frontal plane knee control as peak hip flexion velocity decreased. This study identified electromyographic, kinematic, and ground reaction force evidence that device training improved lower extremity neuromuscular control during single leg lateral drop landing and stabilization. Further studies with other populations are indicated. Copyright © 2010 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    Burroughs, Amelia; Wise, Andrew K; Xiao, Jianqiang; Houghton, Conor; Tang, Tianyu; Suh, Colleen Y; Lang, Eric J; Apps, Richard; Cerminara, Nadia L

    2017-01-01

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

  19. The effect of single twitch and train-of-four stimulation on twitch forces during stable neuromuscular block

    NARCIS (Netherlands)

    van Santen, G; Fidler, [No Value; Houwertjes, MC; Top, WMC; Wierda, JMKH

    2000-01-01

    Objective. We investigated whether the response to a single twitch (ST) stimulus or the first response (T1) to a train-of-four (TOF; 4 stimuli at 2 Hz) stimulus following a stimulus interval of 10 s (i.e., the time between two consecutive ST or TOF stimuli) is influenced by the preceding stimulus in

  20. Effectiveness of different memory training programs on improving hyperphagic behaviors of residents with dementia: a longitudinal single-blind study.

    Science.gov (United States)

    Kao, Chieh-Chun; Lin, Li-Chan; Wu, Shiao-Chi; Lin, Ker-Neng; Liu, Ching-Kuan

    2016-01-01

    Hyperphagia increases eating-associated risks for people with dementia and distress for caregivers. The purpose of this study was to compare the long-term effectiveness of spaced retrieval (SR) training and SR training combined with Montessori activities (SR + M) for improving hyperphagic behaviors of special care unit residents with dementia. The study enrolled patients with dementia suffering from hyperphagia resident in eight institutions and used a cluster-randomized single-blind design, with 46 participants in the SR group, 49 in the SR + M group, and 45 participants in the control group. For these three groups, trained research assistants collected baseline data on hyperphagic behavior, pica, changes in eating habits, short meal frequency, and distress to caregivers. The SR and SR + M groups underwent memory training over a 6-week training period (30 sessions), and a generalized estimating equation was used to compare data of all the three groups of subjects obtained immediately after the training period and at follow-ups 1 month, 3 months, and 6 months later. Results showed that the hyperphagic and pica behaviors of both the SR and SR + M groups were significantly improved (P<0.001) and that the effect lasted for 3 months after training. The improvement of fast eating was significantly superior in the SR + M group than in the SR group. The improvement in distress to caregivers in both intervention groups lasted only until the posttest. Improvement in changes in eating habits of the two groups was not significantly different from that of the control group. SR and SR + M training programs can improve hyperphagic behavior of patients with dementia. The SR + M training program is particularly beneficial for the improvement of rapid eating. Caregivers can choose a suitable memory training program according to the eating problems of their residents.

  1. A motorized pellet dispenser to deliver high intensity training of the single pellet reaching and grasping task in rats.

    Science.gov (United States)

    Torres-Espín, Abel; Forero, Juan; Schmidt, Emma K A; Fouad, Karim; Fenrich, Keith K

    2018-01-15

    The single pellet reaching and grasping (SPG) task is widely used to study forelimb motor performance in rodents and to provide rehabilitation after neurological disorders. Nonetheless, the time necessary to train animals precludes its use in settings where high-intensity training is required. In the current study, we developed a novel high-intensity training protocol for the SPG task based on a motorized pellet dispenser and a dual-window enclosure. We tested the protocol in naive adult rats and found 1) an increase in the intensity of training without increasing the task time and without affecting the overall performance of the animals, 2) a reduction in the variability within and between experiments in comparison to manual SPG training, and 3) a reduction in the time required to conduct experiments. In summary, we developed and tested a novel protocol for SPG training that provides higher-intensity training while reducing the variability of results observed with other protocols. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

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

    2014-10-01

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

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

    Science.gov (United States)

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

    2010-03-09

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

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

    DEFF Research Database (Denmark)

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

    2016-01-01

    visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary...

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

    Science.gov (United States)

    Hwang, Eun Jung; Andersen, Richard A

    2012-02-01

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

  6. Exercise training and weight loss, not always a happy marriage: single blind exercise trials in females with diverse BMI.

    Science.gov (United States)

    Jackson, Matthew; Fatahi, Fardin; Alabduljader, Kholoud; Jelleyman, Charlotte; Moore, Jonathan P; Kubis, Hans-Peter

    2018-04-01

    Individuals show high variability in body weight responses to exercise training. Expectations and motivation towards effects of exercise on body weight might influence eating behaviour and could conceal regulatory mechanisms. We conducted 2 single-blind exercise trials (4 weeks (study 1) and 8 weeks (study 2)) with concealed objectives and exclusion of individuals with weight loss intention. Circuit exercise training programs (3 times a week (45-90 min), intensity 50%-90% peak oxygen uptake for 4 and 8 weeks) were conducted. Thirty-four females finished the 4-week intervention and 36 females the 8-week intervention. Overweight/obese (OV/OB) and lean female participants' weight/body composition responses were assessed and fasting and postprandial appetite hormone levels (PYY, insulin, amylin, leptin, ghrelin) were measured before and after the intervention for understanding potential contribution to individuals' body weight response to exercise training (study 2). Exercise training in both studies did not lead to a significant reduction of weight/body mass index (BMI) in the participants' groups; however, lean participants gained muscle mass. Appetite hormones levels were significantly (p training did not lead to weight loss in female participants, while a considerable proportion of variance in body weight response to training could be explained by individuals' appetite hormone levels and BMI.

  7. Linking investment spikes and productivity growth

    NARCIS (Netherlands)

    Geylani, P.C.; Stefanou, S.E.

    2013-01-01

    We investigate the relationship between productivity growth and investment spikes using Census Bureau’s plant-level dataset for the U.S. food manufacturing industry. There are differences in productivity growth and investment spike patterns across different sub-industries and food manufacturing

  8. The effects of adding single-joint exercises to a multi-joint exercise resistance training program on upper body muscle strength and size in trained men.

    Science.gov (United States)

    de França, Henrique Silvestre; Branco, Paulo Alexandre Nordeste; Guedes Junior, Dilmar Pinto; Gentil, Paulo; Steele, James; Teixeira, Cauê Vazquez La Scala

    2015-08-01

    The aim of this study was compare changes in upper body muscle strength and size in trained men performing resistance training (RT) programs involving multi-joint plus single-joint (MJ+SJ) or only multi-joint (MJ) exercises. Twenty young men with at least 2 years of experience in RT were randomized in 2 groups: MJ+SJ (n = 10; age, 27.7 ± 6.6 years) and MJ (n = 10; age, 29.4 ± 4.6 years). Both groups trained for 8 weeks following a linear periodization model. Measures of elbow flexors and extensors 1-repetition maximum (1RM), flexed arm circumference (FAC), and arm muscle circumference (AMC) were taken pre- and post-training period. Both groups significantly increased 1RM for elbow flexion (4.99% and 6.42% for MJ and MJ+SJ, respectively), extension (10.60% vs 9.79%, for MJ and MJ+SJ, respectively), FAC (1.72% vs 1.45%, for MJ and MJ+SJ, respectively), and AMC (1.33% vs 3.17% for MJ and MJ+SJ, respectively). Comparison between groups revealed no significant difference in any variable. In conclusion, 8 weeks of RT involving MJ or MJ+SJ resulted in similar alterations in muscle strength and size in trained participants. Therefore, the addition of SJ exercises to a RT program involving MJ exercises does not seem to promote additional benefits to trained men, suggesting MJ-only RT to be a time-efficient approach.

  9. Capturing Spike Variability in Noisy Izhikevich Neurons Using Point Process Generalized Linear Models.

    Science.gov (United States)

    Østergaard, Jacob; Kramer, Mark A; Eden, Uri T

    2018-01-01

    To understand neural activity, two broad categories of models exist: statistical and dynamical. While statistical models possess rigorous methods for parameter estimation and goodness-of-fit assessment, dynamical models provide mechanistic insight. In general, these two categories of models are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input current. We then fit these spike train data with a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured.

  10. Enhanced polychronisation in a spiking network with metaplasticity

    Directory of Open Access Journals (Sweden)

    Mira eGuise

    2015-02-01

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

  11. Effectiveness of different memory training programs on improving hyperphagic behaviors of residents with dementia: a longitudinal single-blind study

    Directory of Open Access Journals (Sweden)

    Kao CC

    2016-05-01

    Full Text Available Chieh-Chun Kao,1,2 Li-Chan Lin,3 Shiao-Chi Wu,4 Ker-Neng Lin,5,6 Ching-Kuan Liu7,8 1Department of Nursing, National Yang-Ming University, Taipei, 2Department of Nursing, Ching Kuo Institute of Management and Health, Keelung, 3Institute of Clinical Nursing, 4Institute of Health and Welfare Policy, National Yang-Ming University, 5Neurological Institute, Taipei Veterans General Hospital, Taipei, 6Department of Psychology, Soochow University, Taipei, Taiwan; 7Department of Neurology, Kaohsiung Medical University Hospital, 8Department of Neurology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan Background: Hyperphagia increases eating-associated risks for people with dementia and distress for caregivers. The purpose of this study was to compare the long-term effectiveness of spaced retrieval (SR training and SR training combined with Montessori activities (SR + M for improving hyperphagic behaviors of special care unit residents with dementia. Methods: The study enrolled patients with dementia suffering from hyperphagia resident in eight institutions and used a cluster-randomized single-blind design, with 46 participants in the SR group, 49 in the SR + M group, and 45 participants in the control group. For these three groups, trained research assistants collected baseline data on hyperphagic behavior, pica, changes in eating habits, short meal frequency, and distress to caregivers. The SR and SR + M groups underwent memory training over a 6-week training period (30 sessions, and a generalized estimating equation was used to compare data of all the three groups of subjects obtained immediately after the training period and at follow-ups 1 month, 3 months, and 6 months later. Results: Results showed that the hyperphagic and pica behaviors of both the SR and SR + M groups were significantly improved (P<0.001 and that the effect lasted for 3 months after training. The improvement of fast eating was

  12. Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

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    Kan Li

    2018-04-01

    Full Text Available This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM speech processing as well as neuromorphic implementations based on spiking neural network (SNN, yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR regime.

  13. Single- and multiple-set resistance training improves skeletal and respiratory muscle strength in elderly women

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    Abrahin O

    2014-10-01

    Full Text Available Odilon Abrahin,1–3 Rejane P Rodrigues,1–3 Vanderson C Nascimento,3 Marzo E Da Silva-Grigoletto,1,4 Evitom C Sousa,3 Anderson C Marçal1,2 1Department of Physical Education, Federal University of Sergipe, Sergipe, Brazil; 2Center of Research in Intracellular Signaling, Department of Morphology, Federal University of Sergipe, Sergipe, Brazil; 3Laboratory of Resistance Exercise and Health, Sports Department, University of Pará State, Belem, Brazil; 4Scientific Sport, Sergipe, Brazil Introduction: Aging involves a progressive reduction of respiratory muscle strength as well as muscle strength. Purpose: Compare the effects of resistance training volume on the maximum inspiratory pressure (MIP, maximum expiratory pressure (MEP, functional performance, and muscle strength in elderly women. Methods: Thirty elderly women were randomly assigned to a group performing either single sets (1-SET or three sets (3-SET of exercises. The sit-to-stand test, MIP, MEP, and muscle strength were assessed before and after 24 training sessions. Progressive resistance training was performed two times per week for a total of 8–12 repetitions, using the main muscle groups of the upper and lower limbs. Results: The main results showed that the participants significantly increased their MEP (P<0.05; 1-SET: 34.6%; 3-SET: 35.8% and MIP (P<0.05; 1-SET: 13.7%; 3-SET: 11.2%. Both groups also improved in the sit-to-stand test (P<0.05; 1-SET: 10.6%; 3-SET: 17.1%. After 24 training sessions, muscle strength also significantly increased (P<0.0001; 40%–80% in both groups. An intergroup comparison did not show any statistically significant differences between the groups in any of the parameters analyzed. Conclusion: Single- and multiple-set resistance training programs increased MIP, MEP, muscle strength, and sit-to-stand test performance in elderly women after 24 sessions of training. In conclusion, our results suggested that elderly women who are not in the habit of

  14. Efficient sequential Bayesian inference method for real-time detection and sorting of overlapped neural spikes.

    Science.gov (United States)

    Haga, Tatsuya; Fukayama, Osamu; Takayama, Yuzo; Hoshino, Takayuki; Mabuchi, Kunihiko

    2013-09-30

    Overlapping of extracellularly recorded neural spike waveforms causes the original spike waveforms to become hidden and merged, confounding the real-time detection and sorting of these spikes. Methods proposed for solving this problem include using a multi-trode or placing a restriction on the complexity of overlaps. In this paper, we propose a rapid sequential method for the robust detection and sorting of arbitrarily overlapped spikes recorded with arbitrary types of electrodes. In our method, the probabilities of possible spike trains, including those that are overlapping, are evaluated by sequential Bayesian inference based on probabilistic models of spike-train generation and extracellular voltage recording. To reduce the high computational cost inherent in an exhaustive evaluation, candidates with low probabilities are considered as impossible candidates and are abolished at each sampling time to limit the number of candidates in the next evaluation. In addition, the data from a few subsequent sampling times are considered and used to calculate the "look-ahead probability", resulting in improved calculation efficiency due to a more rapid elimination of candidates. These sufficiently reduce computational time to enable real-time calculation without impairing performance. We assessed the performance of our method using simulated neural signals and actual neural signals recorded in primary cortical neurons cultured on a multi-electrode array. Our results demonstrated that our computational method could be applied in real-time with a delay of less than 10 ms. The estimation accuracy was higher than that of a conventional spike sorting method, particularly for signals with multiple overlapping spikes. Copyright © 2013 Elsevier B.V. All rights reserved.

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

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    Fernando Henrique de Sousa

    2017-03-01

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

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

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    Karim El-Laithy

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Romuald Kosina

    2014-01-01

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

  18. Training With Curved Laparoscopic Instruments in Single-Port Setting Improves Performance Using Straight Instruments: A Prospective Randomized Simulation Study.

    Science.gov (United States)

    Lukovich, Peter; Sionov, Valery Ben; Kakucs, Timea

    2016-01-01

    Lately single-port surgery is becoming a widespread procedure, but it is more difficult than conventional laparoscopy owing to the lack of triangulation. Although, these operations are also possible with standard laparoscopic instruments, curved instruments are being developed. The aims of the study were to identify the effect of training on a box trainer in single-port setting on the quality of acquired skills, and transferred with the straight and curved instruments for the basic laparoscopic tasks, and highlight the importance of a special laparoscopic training curriculum. A prospective study on a box trainer in single-port setting was conducted using 2 groups. Each group performed 2 tasks on the box trainer in single-port setting. Group-S used conventional straight laparoscopic instruments, and Group-C used curved laparoscopic instruments. Learning curves were obtained by daily measurements recorded in 7-day sessions. On the last day, the 2 groups changed instruments between each other. 1st Department of Surgery, Semmelweis University of Medicine from Budapest, Hungary, a university teaching hospital. In all, 20 fifth-year medical students were randomized into 2 groups. None of them had any laparoscopic or endoscopic experience. Participation was voluntary. Although Group-S performed all tasks significantly faster than Group-C on the first day, the difference proved to be nonsignificant on the last day. All participants achieved significantly shorter task completion time on the last day than on the first day, regardless of the instrument they used. Group-S showed improvement of 63.5%, and Group-C 69.0% improvement by the end of the session. After swapping the instruments, Group-S reached significantly higher task completion time with curved instruments, whereas Group-C showed further progression of 8.9% with straight instruments. Training with curved instruments in a single-port setting allows for a better acquisition of skills in a shorter period. For this

  19. Gait analysis following treadmill training with body weight support versus conventional physical therapy: a prospective randomized controlled single blind study.

    Science.gov (United States)

    Lucareli, P R; Lima, M O; Lima, F P S; de Almeida, J G; Brech, G C; D'Andréa Greve, J M

    2011-09-01

    Single-blind randomized, controlled clinical study. To evaluate, using kinematic gait analysis, the results obtained from gait training on a treadmill with body weight support versus those obtained with conventional gait training and physiotherapy. Thirty patients with sequelae from traumatic incomplete spinal cord injuries at least 12 months earlier; patients were able to walk and were classified according to motor function as ASIA (American Spinal Injury Association) impairment scale C or D. Patients were divided randomly into two groups of 15 patients by the drawing of opaque envelopes: group A (weight support) and group B (conventional). After an initial assessment, both groups underwent 30 sessions of gait training. Sessions occurred twice a week, lasted for 30 min each and continued for four months. All of the patients were evaluated by a single blinded examiner using movement analysis to measure angular and linear kinematic gait parameters. Six patients (three from group A and three from group B) were excluded because they attended fewer than 85% of the training sessions. There were no statistically significant differences in intra-group comparisons among the spatial-temporal variables in group B. In group A, the following significant differences in the studied spatial-temporal variables were observed: increases in velocity, distance, cadence, step length, swing phase and gait cycle duration, in addition to a reduction in stance phase. There were also no significant differences in intra-group comparisons among the angular variables in group B. However, group A achieved significant improvements in maximum hip extension and plantar flexion during stance. Gait training with body weight support was more effective than conventional physiotherapy for improving the spatial-temporal and kinematic gait parameters among patients with incomplete spinal cord injuries.

  20. On-chip photonic transistor based on the spike synchronization in circuit QED

    Science.gov (United States)

    Gül, Yusuf

    2018-03-01

    We consider the single photon transistor in coupled cavity system of resonators interacting with multilevel superconducting artificial atom simultaneously. Effective single mode transformation is used for the diagonalization of the Hamiltonian and impedance matching in terms of the normal modes. Storage and transmission of the incident field are described by the interactions between the cavities controlling the atomic transitions of lowest lying states. Rabi splitting of vacuum-induced multiphoton transitions is considered in input/output relations by the quadrature operators in the absence of the input field. Second-order coherence functions are employed to investigate the photon blockade and delocalization-localization transitions of cavity fields. Spontaneous virtual photon conversion into real photons is investigated in localized and oscillating regimes. Reflection and transmission of cavity output fields are investigated in the presence of the multilevel transitions. Accumulation and firing of the reflected and transmitted fields are used to investigate the synchronization of the bunching spike train of transmitted field and population imbalance of cavity fields. In the presence of single photon gate field, gain enhancement is explained for transmitted regime.

  1. Exact subthreshold integration with continuous spike times in discrete-time neural network simulations.

    Science.gov (United States)

    Morrison, Abigail; Straube, Sirko; Plesser, Hans Ekkehard; Diesmann, Markus

    2007-01-01

    Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid can have undesirable consequences, which has led to interest in interpolating spike times between the grid points to retrieve an adequate representation of network dynamics. We demonstrate that the exact integration scheme can be combined naturally with off-grid spike events found by interpolation. We show that by exploiting the existence of a minimal synaptic propagation delay, the need for a central event queue is removed, so that the precision of event-driven simulation on the level of single neurons is combined with the efficiency of time-driven global scheduling. Further, for neuron models with linear subthreshold dynamics, even local event queuing can be avoided, resulting in much greater efficiency on the single-neuron level. These ideas are exemplified by two implementations of a widely used neuron model. We present a measure for the efficiency of network simulations in terms of their integration error and show that for a wide range of input spike rates, the novel techniques we present are both more accurate and faster than standard techniques.

  2. Technology-aware algorithm design for neural spike detection, feature extraction, and dimensionality reduction.

    Science.gov (United States)

    Gibson, Sarah; Judy, Jack W; Marković, Dejan

    2010-10-01

    Applications such as brain-machine interfaces require hardware spike sorting in order to 1) obtain single-unit activity and 2) perform data reduction for wireless data transmission. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection, feature-extraction, and dimensionality-reduction algorithms for spike sorting are described and evaluated in terms of accuracy versus complexity. The nonlinear energy operator is chosen as the optimal spike-detection algorithm, being most robust over noise and relatively simple. Discrete derivatives is chosen as the optimal feature-extraction method, maintaining high accuracy across signal-to-noise ratios with a complexity orders of magnitude less than that of traditional methods such as principal-component analysis. We introduce the maximum-difference algorithm, which is shown to be the best dimensionality-reduction method for hardware spike sorting.

  3. Macroscopic Description for Networks of Spiking Neurons

    Science.gov (United States)

    Montbrió, Ernest; Pazó, Diego; Roxin, Alex

    2015-04-01

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

  4. A Meta-Analysis of Single-Case Studies on Functional Communication Training

    Science.gov (United States)

    Heath, Amy Kathleen

    2012-01-01

    Functional Communication Training (FCT) is an intervention that involves teaching a communicative response to decrease the occurrence of challenging behavior in individuals with disabilities. FCT is a two step intervention in which the interventionist first determines the function, or purpose, of the challenging behavior and then teaches a…

  5. Efficient Architecture for Spike Sorting in Reconfigurable Hardware

    Science.gov (United States)

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

    2013-01-01

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

  6. Efficient Architecture for Spike Sorting in Reconfigurable Hardware

    Directory of Open Access Journals (Sweden)

    Sheng-Ying Lai

    2013-11-01

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

  7. Linking structure and activity in nonlinear spiking networks.

    Directory of Open Access Journals (Sweden)

    Gabriel Koch Ocker

    2017-06-01

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

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

    Science.gov (United States)

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

    2016-10-01

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

  9. A pilot study of a single-session training to promote mindful eating.

    Science.gov (United States)

    Jacobs, Jayme; Cardaciotto, LeeAnn; Block-Lerner, Jennifer; McMahon, Cori

    2013-01-01

    Although researchers have not yet examined the applicability of mindfulness for weight-gain prevention, mindfulness training has the potential to increase an individual's awareness of factors that enable an individual to avoid weight gain caused by overconsumption. The study intended to examine the effects of 1 h of mindfulness training on state mindfulness and food consumption. The research team performed a pilot study. The study occurred at an urban, northeastern, Catholic university. Participants were 26 undergraduate, English-speaking students who were at least 18 y old (77% female, 73% Caucasian). Students with food allergies, an inability to fast, or a current or past diagnosis of an eating disorder were ineligible. Participants fasted for 4 h. Between the third and fourth hours, they attended a 1-h session of mindfulness training that integrated three experiential mindfulness exercises with group discussion. Following training, they applied the skills they learned during a silent lunch. The Toronto Mindfulness Scale (TMS), the Awareness subscale of the Philadelphia Mindfulness Scale (PHLMS-AW), and a modified version of the Acting with Awareness subscale of the Five-Facet Mindfulness Questionnaire (FFMQ-AW) were used preand posttraining to assess changes in state mindfulness, present-moment awareness, and mealtime awareness, respectively. A postmeal, subjective hunger/fullness Likert scale was used to assess food consumption (healthy vs unhealthy consumption). The study found a statistically significant increase in state mindfulness (P=.002). Eighty-six percent of participants engaged in healthy food consumption. No statistically significant changes occurred in either present-moment awareness (P=.617) or mealtime awareness (P=.483). Preliminary results suggest promising benefits for use of mindfulness training on weight-gain prevention in healthy individuals. More research is needed to understand the impact that mindfulness may have on long-term, weight

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

    Science.gov (United States)

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

    2015-04-30

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

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

    Science.gov (United States)

    Städele, Carola; Stein, Wolfgang

    2016-06-22

    a single-cell sensory neuron in the stomatogastric nervous system. Action potentials were consistently initiated at a specific region of the axon trunk, near a motor neuropil. Spike frequency was regulated by motor neuron activity, but only if spike initiation occurred at this location. Neuromodulation of the axon dislocated the site of initiation, resulting in abolishment of signal integration from motor neurons. Thus, neuromodulation allows for a dynamic adjustment of axonal signal integration. Copyright © 2016 the authors 0270-6474/16/366718-14$15.00/0.

  12. Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

    Science.gov (United States)

    Leibig, Christian; Wachtler, Thomas; Zeck, Günther

    2016-09-15

    Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons. Here we present a spike sorting algorithm based on convolutive ICA (cICA) to retrieve a larger number of accurately sorted neurons than with instantaneous ICA while accounting for signal overlaps. Spike sorting was applied to datasets with varying signal-to-noise ratios (SNR: 3-12) and 27% spike overlaps, sampled at either 11.5 or 23kHz on 4365 electrodes. We demonstrate how the instantaneity assumption in ICA-based algorithms has to be relaxed in order to improve the spike sorting performance for high-density microelectrode array recordings. Reformulating the convolutive mixture as an instantaneous mixture by modeling several delayed samples jointly is necessary to increase signal-to-noise ratio. Our results emphasize that different cICA algorithms are not equivalent. Spike sorting performance was assessed with ground-truth data generated from experimentally derived templates. The presented spike sorter was able to extract ≈90% of the true spike trains with an error rate below 2%. It was superior to two alternative (c)ICA methods (≈80% accurately sorted neurons) and comparable to a supervised sorting. Our new algorithm represents a fast solution to overcome the current bottleneck in spike sorting of large datasets generated by simultaneous recording with thousands of electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. ANKLE JOINT CONTROL DURING SINGLE-LEGGED BALANCE USING COMMON BALANCE TRAINING DEVICES - IMPLICATIONS FOR REHABILITATION STRATEGIES

    DEFF Research Database (Denmark)

    Strøm, Mark; Thorborg, Kristian; Bandholm, Thomas

    2016-01-01

    BACKGROUND: A lateral ankle sprain is the most prevalent musculoskeletal injury in sports. Exercises that aim to improve balance are a standard part of the ankle rehabilitation process. In an optimal progression model for ankle rehabilitation and prevention of future ankle sprains, it is important...... to characterize different balance exercises based on level of difficulty and sensori-motor training stimulus. PURPOSE: The purpose of this study was to investigate frontal-plane ankle kinematics and associated peroneal muscle activity during single-legged balance on stable surface (floor) and three commonly used...... balance devices (Airex®, BOSU® Ball and wobble board). DESIGN: Descriptive exploratory laboratory study. METHODS: Nineteen healthy subjects performed single-legged balance with eyes open on an Airex® mat, BOSU® Ball, wobble board, and floor (reference condition). Ankle kinematics were measured using...

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

    Science.gov (United States)

    Sharafi, Nahal; Benda, Jan; Lindner, Benjamin

    2013-04-01

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

  15. Reconstructing stimuli from the spike-times of leaky integrate and fire neurons

    Directory of Open Access Journals (Sweden)

    Sebastian eGerwinn

    2011-02-01

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

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

    Science.gov (United States)

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

    2008-07-01

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

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

    Science.gov (United States)

    Ruf, B; Schmitt, M

    1998-01-01

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

  18. Immediate effects of a single session of robot-assisted gait training using Hybrid Assistive Limb (HAL) for cerebral palsy.

    Science.gov (United States)

    Matsuda, Mayumi; Mataki, Yuki; Mutsuzaki, Hirotaka; Yoshikawa, Kenichi; Takahashi, Kazushi; Enomoto, Keiko; Sano, Kumiko; Mizukami, Masafumi; Tomita, Kazuhide; Ohguro, Haruka; Iwasaki, Nobuaki

    2018-02-01

    [Purpose] Robot-assisted gait training (RAGT) using Hybrid Assistive Limb (HAL, CYBERDYNE) was previously reported beneficial for stroke and spinal cord injury patients. Here, we investigate the immediate effect of a single session of RAGT using HAL on gait function for cerebral palsy (CP) patients. [Subjects and Methods] Twelve patients (average age: 16.2 ± 7.3 years) with CP received a single session of RAGT using HAL. Gait speed, step length, cadence, single-leg support per gait cycle, hip and knee joint angle in stance, and swing phase per gait cycle were assessed before, during, and immediately after HAL intervention. [Results] Compared to baseline values, single-leg support per gait cycle (64.5 ± 15.8% to 69.3 ± 12.1%), hip extension angle in mid-stance (149.2 ± 19.0° to 155.5 ± 20.1°), and knee extension angle in mid-stance (137.6 ± 20.2° to 143.1 ± 19.5°) were significantly increased immediately after intervention. Further, the knee flexion angle in mid-swing was significantly decreased immediately after treatment (112.0 ± 15.5° to 105.2 ± 17.1°). Hip flexion angle in mid-swing also decreased following intervention (137.2 ± 14.6° to 129.7 ± 16.6°), but not significantly. Conversely, gait speed, step length, and cadence were unchanged after intervention. [Conclusion] A single-time RAGT with HAL improved single-leg support per gait cycle and hip and knee joint angle during gait, therapeutically improving gait function in CP patients.

  19. Preservice Teachers' Computer Use in Single Computer Training Courses; Relationships and Predictions

    Science.gov (United States)

    Zogheib, Salah

    2015-01-01

    Single computer courses offered at colleges of education are expected to provide preservice teachers with the skills and expertise needed to adopt computer technology in their future classrooms. However, preservice teachers still find difficulty adopting such technology. This research paper investigated relationships among preservice teachers'…

  20. Identifying types of physical activity with a single accelerometer: Evaluating laboratory trained algorithms in daily life

    NARCIS (Netherlands)

    Cuba Gyllensten, I.; Bonomi, A.G.

    2011-01-01

    Accurate identification of physical activity types has been achieved in laboratory conditions using single-site accelerometers and classification algorithms. This methodology is then applied to free-living subjects to determine activity behaviour. This study aimed at analysing the reproducibility of

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

    Science.gov (United States)

    Nobukawa, Sou; Nishimura, Haruhiko

    2016-08-01

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

  2. Single leg jumping neuromuscular control is improved following whole body, long-axis rotational training.

    Science.gov (United States)

    Nyland, John; Burden, Robert; Krupp, Ryan; Caborn, David N M

    2011-04-01

    Improved lower extremity neuromuscular control during sports may decrease injury risk. This prospective study evaluated progressive resistance, whole body, long-axis rotational training on the Ground Force 360 device. Our hypothesis was that device training would improve lower extremity neuromuscular control based on previous reports of kinematic, ground reaction force (GRF) or electromyographic (EMG) evidence of safer or more efficient dynamic knee stability during jumping. Thirty-six healthy subjects were randomly assigned to either training (Group 1) or control (Group 2) groups. Using a pre-test, post-test study design data were collected from three SLVJ trials. Unpaired t-tests with adjustments for multiple comparisons were used to evaluate group mean change differences (P≤0.05/25≤0.002). During propulsion Group 1 standardized EMG amplitude mean change differences for gluteus maximus (-21.8% vs. +17.4%), gluteus medius (-28.6% vs. +15.0%), rectus femoris (-27.1% vs. +11.2%), vastus medialis (-20.2% vs. +9.1%), and medial hamstrings (-38.3% vs. +30.3%) differed from Group 2. During landing Group 1 standardized EMG amplitude mean change differences for gluteus maximus (-32.9% vs. +11.1%) and rectus femoris (-33.3% vs. +29.0%) also differed from Group 2. Group 1 peak propulsion vertical GRF (+0.24N/kg vs. -0.46N/kg) and landing GRF stabilization timing (-0.68 vs. +0.05s) mean change differences differed from Group 2. Group 1 mean hip (-16.3 vs. +7.8°/s) and knee (-21.4 vs. +18.5°/s) flexion velocity mean change differences also differed from Group 2. Improved lower extremity neuromuscular efficiency, increased peak propulsive vertical GRF, decreased mean hip and knee flexion velocities during landing, and earlier landing stabilization timing in the training group suggests improved lower extremity neuromuscular control. Copyright © 2010 Elsevier Ltd. All rights reserved.

  3. Neural spike sorting using iterative ICA and a deflation-based approach.

    Science.gov (United States)

    Tiganj, Z; Mboup, M

    2012-12-01

    We propose a spike sorting method for multi-channel recordings. When applied in neural recordings, the performance of the independent component analysis (ICA) algorithm is known to be limited, since the number of recording sites is much lower than the number of neurons. The proposed method uses an iterative application of ICA and a deflation technique in two nested loops. In each iteration of the external loop, the spiking activity of one neuron is singled out and then deflated from the recordings. The internal loop implements a sequence of ICA and sorting for removing the noise and all the spikes that are not fired by the targeted neuron. Then a final step is appended to the two nested loops in order to separate simultaneously fired spikes. We solve this problem by taking all possible pairs of the sorted neurons and apply ICA only on the segments of the signal during which at least one of the neurons in a given pair was active. We validate the performance of the proposed method on simulated recordings, but also on a specific type of real recordings: simultaneous extracellular-intracellular. We quantify the sorting results on the extracellular recordings for the spikes that come from the neurons recorded intracellularly. The results suggest that the proposed solution significantly improves the performance of ICA in spike sorting.

  4. Perturbation During Treadmill Training Improves Dynamic Balance and Gait in Parkinson's Disease: A Single-Blind Randomized Controlled Pilot Trial.

    Science.gov (United States)

    Steib, Simon; Klamroth, Sarah; Gaßner, Heiko; Pasluosta, Cristian; Eskofier, Björn; Winkler, Jürgen; Klucken, Jochen; Pfeifer, Klaus

    2017-08-01

    Gait and balance dysfunction are major symptoms in Parkinson's disease (PD). Treadmill training improves gait characteristics in this population but does not reflect the dynamic nature of controlling balance during ambulation in everyday life contexts. To evaluate whether postural perturbations during treadmill walking lead to superior effects on gait and balance performance compared with standard treadmill training. In this single-blind randomized controlled trial, 43 PD patients (Hoehn & Yahr stage 1-3.5) were assigned to either an 8-week perturbed treadmill intervention (n = 21) or a control group (n = 22) training on the identical treadmill without perturbations. Patients were assessed at baseline, postintervention, and at 3 months' follow-up. Primary endpoints were overground gait speed and balance (Mini-BESTest). Secondary outcomes included fast gait speed, walking capacity (2-Minute Walk Test), dynamic balance (Timed Up-and-Go), static balance (postural sway), and balance confidence (Activities-Specific Balance Confidence [ABC] scale). There were no significant between-group differences in change over time for the primary outcomes. At postintervention, both groups demonstrated similar improvements in overground gait speed ( P = .009), and no changes in the Mini-BESTest ( P = .641). A significant group-by-time interaction ( P = .048) existed for the Timed Up-and-Go, with improved performance only in the perturbation group. In addition, the perturbation but not the control group significantly increased walking capacity ( P = .038). Intervention effects were not sustained at follow-up. Our primary findings suggest no superior effect of perturbation training on gait and balance in PD patients. However, some favorable trends existed for secondary gait and dynamic balance parameters, which should be investigated in future trials.

  5. Epileptiform spike detection via convolutional neural networks

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

    2016-01-01

    The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...... fashion. The CNN has a convolutional architecture with filters of various sizes applied to the input layer, leaky ReLUs as activation functions, and a sigmoid output layer. Balanced mini-batches were applied to handle the imbalance in the data set. Leave-one-patient-out cross-validation was carried out...... to test the CNN and benchmark models on EEG data of five epilepsy patients. We achieved 0.947 AUC for the CNN, while the best performing benchmark model, Support Vector Machines with Gaussian kernel, achieved an AUC of 0.912....

  6. The electric potential of tripolar spikes

    International Nuclear Information System (INIS)

    Nocera, L.

    2010-01-01

    We present an analytical formula for the waveform of the electric potential associated with a tripolar spike in a plasma. This formula is based on the construction and on the subsequent solution of a differential equation for the waveform. We work out this equation as a direct consequence of the morphological and functional properties of the observed waveform, without making any reference to the velocity distributions of the electrons and of the ions which sustain the spike. In the approximation of small potential amplitudes, we solve this equation by quadrature. In particular, in the second order approximation, the solution of this equation is given in terms of elementary functions. This analytical solution is able to reproduce the potential waveforms associated with electron holes, ion holes, monotonic and nonmonotonic double layers and tripolar spikes, in excellent agreement with observations.

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

    Science.gov (United States)

    Melchior, Peter; Spergel, David; Lanz, Arianna

    2018-02-01

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

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

    International Nuclear Information System (INIS)

    Fiete, Ila R.; Seung, H. Sebastian

    2006-01-01

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

  9. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

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

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

    Science.gov (United States)

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

    2017-04-01

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

  11. Development and evaluation of a modified brief assertiveness training for nurses in the workplace: a single-group feasibility study.

    Science.gov (United States)

    Nakamura, Yohei; Yoshinaga, Naoki; Tanoue, Hiroki; Kato, Sayaka; Nakamura, Sayoko; Aoishi, Keiko; Shiraishi, Yuko

    2017-01-01

    Effective communication has a great impact on nurses' job satisfaction, team relationships, as well as patient care/safety. Previous studies have highlighted the various beneficial effects of enhancing communication through assertiveness training programs for nurses. However, most programs take a long time to implement; thus, briefer programs are urgently required for universal on-the-job-training in the workplace. The purpose of this feasibility study was to develop and evaluate a modified brief assertiveness training program (with cognitive techniques) for nurses in the workplace. This study was carried out as a single-group, open trial (pre-post comparison without a control group). Registered nurses and assistant nurses, working at two private psychiatric hospitals in Miyazaki Prefecture in Japan, were recruited. After enrolling in the study, participants received a program of two 90-min sessions with a 1-month interval between sessions. The primary outcome was the Rathus Assertiveness Schedule (RAS), with secondary measurements using the Brief Version of the Fear of Negative Evaluation Scale (BFNE) and the Brief Job Stress Questionnaire (BJSQ). Assessments were conducted at baseline and after a 1-month interval (pre- and post-intervention). A total of 22 participants enrolled in the study and completed the program. The mean total score on the primary outcome (RAS) significantly improved from -12.9 (SD = 17.2) to -8.6 (SD = 18.6) ( p  = 0.01). The within-group effect size at the post-intervention was Cohen's d = 0.24; this corresponds to the small effect of the program. Regarding secondary outcomes, there were no statistically significant effects on the BFNE or any of the BJSQ subscales (job-stressors, psychological distress, physical distress, worksite support, and satisfaction). This single-group feasibility study demonstrated that our modified brief assertiveness training for nurses seems feasible and may achieve a favorable outcome in improving their

  12. Rotational Angles and Velocities During Down the Line and Diagonal Across Court Volleyball Spikes

    Directory of Open Access Journals (Sweden)

    Justin R. Brown

    2014-05-01

    Full Text Available The volleyball spike is an explosive movement that is frequently used to end a rally and earn a point. High velocity spikes are an important skill for a successful volleyball offense. Although the influence of vertical jump height and arm velocity on spiked ball velocity (SBV have been investigated, little is known about the relationship of shoulder and hip angular kinematics with SBV. Other sport skills, like the baseball pitch share similar movement patterns and suggest trunk rotation is important for such movements. The purpose of this study was to examine the relationship of both shoulder and hip angular kinematics with ball velocity during the volleyball spike. Methods: Fourteen Division I collegiate female volleyball players executed down the line (DL and diagonally across-court (DAC spikes in a laboratory setting to measure shoulder and hip angular kinematics and velocities. Each spike was analyzed using a 10 Camera Raptor-E Digital Real Time Camera System.  Results: DL SBV was significantly greater than for DAC, respectively (17.54±2.35 vs. 15.97±2.36 m/s, p<0.05.  The Shoulder Hip Separation Angle (S-HSA, Shoulder Angular Velocity (SAV, and Hip Angular Velocity (HAV were all significantly correlated with DAC SBV. S-HSA was the most significant predictor of DAC SBV as determined by regression analysis.  Conclusions: This study provides support for a relationship between a greater S-HSA and SBV. Future research should continue to 1 examine the influence of core training exercise and rotational skill drills on SBV and 2 examine trunk angular velocities during various types of spikes during play.

  13. Spike-timing theory of working memory.

    Directory of Open Access Journals (Sweden)

    Botond Szatmáry

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

  14. Physics of volleyball: Spiking with a purpose

    Science.gov (United States)

    Behroozi, F.

    1998-05-01

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

  15. Investment spikes in Dutch greenhouse horticulture

    NARCIS (Netherlands)

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

    2008-01-01

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

  16. Food Price Spikes, Price Insulation, and Poverty

    OpenAIRE

    Anderson, Kym; Ivanic, Maros; Martin, Will

    2013-01-01

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

  17. Gymnosporia montana Benth.(Mountain Spike Thorn)

    Indian Academy of Sciences (India)

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

  18. Positive and negative staircase effect during single twitch and train-of-four stimulation: a laboratory study in dogs.

    Science.gov (United States)

    Martin-Flores, Manuel; Tseng, Chia T; Sakai, Daniel M; Romano, Marta; Campoy, Luis; Gleed, Robin D

    2017-04-01

    A positive staircase effect is well documented during neuromuscular monitoring. However, the increase in twitch amplitude may not remain stable over time. We compared the staircase phenomenon and twitch stability during single twitch (ST) or train-of-four (TOF) stimulation in anesthetized dogs. Force of contraction was measured in ten dogs. Each thoracic limb was stimulated with ST 0.1 Hz or TOF q 12 s for 25 min (random order). No neuromuscular blockers were administered. Every 5 min, ST and T1 amplitudes were compared within and between groups. Stability of twitch amplitude (5 % with TOF. An initial increase in ST amplitude remained stable over the observation period, but the increase in T1 amplitude during TOF was frequently followed by a decay. A stable twitch amplitude (variation twitch amplitude.

  19. ANKLE JOINT CONTROL DURING SINGLE-LEGGED BALANCE USING COMMON BALANCE TRAINING DEVICES - IMPLICATIONS FOR REHABILITATION STRATEGIES

    DEFF Research Database (Denmark)

    Strøm, Mark; Thorborg, Kristian; Bandholm, Thomas

    2016-01-01

    to characterize different balance exercises based on level of difficulty and sensori-motor training stimulus. PURPOSE: The purpose of this study was to investigate frontal-plane ankle kinematics and associated peroneal muscle activity during single-legged balance on stable surface (floor) and three commonly used...... compared to Airex® and floor. This study can serve as guidance for clinicians who wish to implement a gradual progression of ankle rehabilitation and prevention exercises by taking the related ankle kinematics and muscle activity into account. LEVEL OF EVIDENCE: Level 3.......BACKGROUND: A lateral ankle sprain is the most prevalent musculoskeletal injury in sports. Exercises that aim to improve balance are a standard part of the ankle rehabilitation process. In an optimal progression model for ankle rehabilitation and prevention of future ankle sprains, it is important...

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

    Science.gov (United States)

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

    2006-01-15

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

  1. Treatment of visuospatial neglect with biparietal tDCS and cognitive training: a single-case study

    Directory of Open Access Journals (Sweden)

    Anna-Katharine eBrem

    2014-09-01

    Full Text Available Symptoms of visuospatial neglect occur frequently after unilateral brain damage. Neglect hampers rehabilitation progress and is associated with reduced quality of life. However, existing treatment methods show limited efficacy. Transcranial direct current stimulation (tDCS is a neuromodulatory technique, which can be used to increase or decrease brain excitability. Its combination with conventional neglect therapy may enhance treatment efficacy.A 72-year-old male with a subacute ischaemic stroke of the right posterior cerebral artery suffering from visuospatial neglect, hemianopia, and hemiparesis was treated with biparietal tDCS and cognitive neglect therapy in a double-blind, sham-controlled single-case study. Four weeks of daily treatment sessions (5 days per week, 30 min were started 26 days post-stroke. During week 1 and 4 the patient received conventional neglect therapy, during week 2, conventional neglect therapy was combined once with sham and once with real biparietal tDCS. Week 3 consisted of daily sessions of real biparietal tDCS (1 mA, 20 min combined with neglect therapy. Outcome measures were assessed before, immediately after, as well as 1 week and 3 months after the end of treatment. They included subtests of the Test for Attentional Performance (TAP: covert attention (main outcome, alertness, visual field; the Neglect-Test (NET: line bisection, cancellation, copying; and activities of daily living (ADL. After real stimulation, covert attention allocation towards left-sided invalid stimuli was significantly improved, and line bisection and copying improved qualitatively as compared to sham stimulation. ADL were only improved at the 3-month follow-up. This single-case study demonstrates for the first time that combined application of tDCS and cognitive training may enhance training-induced improvements in measures of visuospatial neglect and is applicable in a clinical context.

  2. Exposure to plastic surgery during undergraduate medical training: A single-institution review.

    Science.gov (United States)

    Austin, Ryan E; Wanzel, Kyle R

    2015-01-01

    Applications to surgical residency programs have declined over the past decade. Even highly competitive programs, such as plastic surgery, have begun to witness these effects. Studies have shown that early surgical exposure has a positive influence on career selection. To review plastic surgery application trends across Canada, and to further investigate medical student exposure to plastic surgery. To examine plastic surgery application trends, national data from the Canadian Resident Matching Service database were analyzed, comparing 2002 to 2007 with 2008 to 2013. To evaluate plastic surgery exposure, a survey of all undergraduate medical students at the University of Toronto (Toronto, Ontario) during the 2012/2013 academic year was conducted. Comparing 2002 to 2007 and 2008 to 2013, the average number of national plastic surgery training positions nearly doubled, while first-choice applicants decreased by 15.3%. The majority of Canadian academic institutions experienced a decrease in first-choice applicants; 84.7% of survey respondents indicated they had no exposure to plastic surgery during their medical education. Furthermore, 89.7% believed their education had not provided a basic understanding of issues commonly managed by plastic surgeons. The majority of students indicated they receive significantly less plastic surgery teaching than all other surgical subspecialties. More than 44% of students not considering plastic surgery as a career indicated they may be more likely to with increased exposure. If there is a desire to grow the specialty through future generations, recruiting tactics to foster greater interest in plastic surgery must be altered. The present study suggests increased and earlier exposure for medical students is a potential solution.

  3. Exposure to plastic surgery during undergraduate medical training: A single-institution review

    Science.gov (United States)

    Austin, Ryan E; Wanzel, Kyle R

    2015-01-01

    BACKGROUND: Applications to surgical residency programs have declined over the past decade. Even highly competitive programs, such as plastic surgery, have begun to witness these effects. Studies have shown that early surgical exposure has a positive influence on career selection. OBJECTIVE: To review plastic surgery application trends across Canada, and to further investigate medical student exposure to plastic surgery. METHODS: To examine plastic surgery application trends, national data from the Canadian Resident Matching Service database were analyzed, comparing 2002 to 2007 with 2008 to 2013. To evaluate plastic surgery exposure, a survey of all undergraduate medical students at the University of Toronto (Toronto, Ontario) during the 2012/2013 academic year was conducted. RESULTS: Comparing 2002 to 2007 and 2008 to 2013, the average number of national plastic surgery training positions nearly doubled, while first-choice applicants decreased by 15.3%. The majority of Canadian academic institutions experienced a decrease in first-choice applicants; 84.7% of survey respondents indicated they had no exposure to plastic surgery during their medical education. Furthermore, 89.7% believed their education had not provided a basic understanding of issues commonly managed by plastic surgeons. The majority of students indicated they receive significantly less plastic surgery teaching than all other surgical subspecialties. More than 44% of students not considering plastic surgery as a career indicated they may be more likely to with increased exposure. CONCLUSION: If there is a desire to grow the specialty through future generations, recruiting tactics to foster greater interest in plastic surgery must be altered. The present study suggests increased and earlier exposure for medical students is a potential solution. PMID:25821773

  4. Effects of a Single Session of High Intensity Interval Treadmill Training on Corticomotor Excitability following Stroke: Implications for Therapy

    Directory of Open Access Journals (Sweden)

    Sangeetha Madhavan

    2016-01-01

    Full Text Available Objective. High intensity interval treadmill training (HIITT has been gaining popularity for gait rehabilitation after stroke. In this study, we examined the changes in excitability of the lower limb motor cortical representation (M1 in chronic stroke survivors following a single session of HIITT. We also determined whether exercise-induced changes in excitability could be modulated by transcranial direct current stimulation (tDCS enhanced with a paretic ankle skill acquisition task. Methods. Eleven individuals with chronic stroke participated in two 40-minute treadmill-training sessions: HIITT alone and HITT preceded by anodal tDCS enhanced with a skill acquisition task (e-tDCS+HIITT. Transcranial magnetic stimulation (TMS was used to assess corticomotor excitability of paretic and nonparetic tibialis anterior (TA muscles. Results. HIIT alone reduced paretic TA M1 excitability in 7 of 11 participants by ≥ 10%. e-tDCS+HIITT increased paretic TA M1 excitability and decreased nonparetic TA M1 excitability. Conclusions. HIITT suppresses corticomotor excitability in some people with chronic stroke. When HIITT is preceded by tDCS in combination with a skill acquisition task, the asymmetry of between-hemisphere corticomotor excitability is reduced. Significance. This study provides preliminary data indicating that the cardiovascular benefits of HIITT may be achieved without suppressing motor excitability in some stroke survivors.

  5. Non-parametric method for separating domestic hot water heating spikes and space heating

    DEFF Research Database (Denmark)

    Bacher, Peder; de Saint-Aubain, Philip Anton; Christiansen, Lasse Engbo

    2016-01-01

    In this paper a method for separating spikes from a noisy data series, where the data change and evolve over time, is presented. The method is applied on measurements of the total heat load for a single family house. It relies on the fact that the domestic hot water heating is a process generatin...

  6. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

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

    2017-12-01

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

  7. Single-site robotic cholecystectomy and robotics training: should we start in the junior years?

    Science.gov (United States)

    Ayabe, Reed I; Parrish, Aaron B; Dauphine, Christine E; Hari, Danielle M; Ozao-Choy, Junko J

    2018-04-01

    It has become increasingly important to expose surgical residents to robotic surgery as its applications continue to expand. Single-site robotic cholecystectomy (SSRC) is an excellent introductory case to robotics. Resident involvement in SSRC is known to be feasible. Here, we sought to determine whether it is safe to introduce SSRC to junior residents. A total of 98 SSRC cases were performed by general surgery residents between August 2015 and August 2016. Cases were divided into groups based on resident level: second- and third-years (juniors) versus fourth- and fifth-years (seniors). Patient age, gender, race, body mass index, and comorbidities were recorded. The number of prior laparoscopic cholecystectomies completed by participating residents was noted. Outcomes including operative time, console time, rate of conversion to open cholecystectomy, and complication rate were compared between groups. Juniors performed 54 SSRC cases, whereas seniors performed 44. There were no significant differences in patient age, gender, race, body mass index, or comorbidities between the two groups. Juniors had less experience with laparoscopic cholecystectomy. There was no significant difference in mean operative time (92.7 min versus 98.0 min, P = 0.254), console time (48.7 min versus 50.8 min, P = 0.639), or complication rate (3.7% versus 2.3%, P = 0.68) between juniors and seniors. SSRC is an excellent way to introduce general surgery residents to robotics. This study shows that with attending supervision, SSRC is feasible and safe for both junior and senior residents with very low complication rates and no adverse effect on operative time. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. AN OVERVIEW OF TRAINING METHODS THAT PROMOTE THE HIGHEST LIPID OXIDATION DURING AND AFTER A SINGLE EXERCISE SESSION

    Directory of Open Access Journals (Sweden)

    Barbara Purkart

    2016-02-01

    Full Text Available Given that physical activity is the most effective way to increase lipid oxidation, its effects are influenced by several factors. The goal of this review was to identify the most effective methods that facilitate the highest lipid oxidation during and after a single exercise session. For this purpose, the available scientific literature was examined using PubMed, Web of Science, Google Scholar and Cochrane Library databases up to June 2013 with the following keywords: excess post exercise oxygen consumption, exercise fatty acid, energy expenditure exercise and interval training. From the identified 48,583 potentially relevant references, 172 of them met all the required criteria. It was found out that prolonged (> 30 min moderate intensity (55 − 70 % VO2max exercise such as walking, jogging or cycling is the most effective way to increase lipid oxidation during and after a single exercise session. Low-volume high-intensity interval exercise is supposed to be as effective as traditional exercise with continuous endurance, with the main effect on lipid oxidation after the session and similar long-term metabolic adaptations. However, more research is still needed to compare the effects of regular resistance exercise with traditional endurance and high-intensity interval exercise. Finally, nutrition is also a significant factor since food rich in fat and low in carbohydrates promotes greater lipid oxidation.

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

    Science.gov (United States)

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

    2016-01-01

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

  10. Effects of jump and balance training on knee kinematics and electromyography of female basketball athletes during a single limb drop landing: pre-post intervention study.

    Science.gov (United States)

    Nagano, Yasuharu; Ida, Hirofumi; Akai, Masami; Fukubayashi, Toru

    2011-07-14

    Some research studies have investigated the effects of anterior cruciate ligament (ACL) injury prevention programs on knee kinematics during landing tasks; however the results were different among the studies. Even though tibial rotation is usually observed at the time of ACL injury, the effects of training programs for knee kinematics in the horizontal plane have not yet been analyzed. The purpose of this study was to determine the effects of a jump and balance training program on knee kinematics including tibial rotation as well as on electromyography of the quadriceps and hamstrings in female athletes. Eight female basketball athletes participated in the experiment. All subjects performed a single limb landing at three different times: the initial test, five weeks later, and one week after completing training. The jump and balance training program lasted for five weeks. Knee kinematics and simultaneous electromyography of the rectus femoris and Hamstrings before training were compared with those measured after completing the training program. After training, regarding the position of the knee at foot contact, the knee flexion angle for the Post-training trial (mean (SE): 24.4 (2.1) deg) was significantly larger than that for the Pre-training trial (19.3 (2.5) deg) (p training trial (40.2 (1.9) deg) was significantly larger than that for the Pre-training trial (34.3 (2.5) deg) (p training. A significant increase was also found in the activity of the hamstrings 50 ms before foot contact (p jump and balance training program successfully increased knee flexion and hamstring activity of female athletes during landing, and has the possibility of producing partial effects to avoid the characteristic knee position observed in ACL injury, thereby preventing injury. However, the expected changes in frontal and transverse kinematics of the knee were not observed.

  11. The effects of multi-domain versus single-domain cognitive training in non-demented older people: a randomized controlled trial

    Directory of Open Access Journals (Sweden)

    Cheng Yan

    2012-03-01

    Full Text Available Abstract Background Whether healthy older people can benefit from cognitive training (CogTr remains controversial. This study explored the benefits of CogTr in community dwelling, healthy, older adults and compared the effects of single-domain with multi-domain CogTr interventions. Methods A randomized, controlled, 3-month trial of CogTr with double-blind assessments at baseline and immediate, 6-month and 12-month follow-up after training completion was conducted. A total of 270 healthy Chinese older people, 65 to 75 years old, were recruited from the Ganquan-area community in Shanghai. Participants were randomly assigned to three groups: multi-domain CogTr, single-domain CogTr, and a wait-list control group. Twenty-four sessions of CogTr were administrated to the intervention groups over a three-month period. Six months later, three booster training sessions were offered to 60% of the initial training participants. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS, Form A, the Color Word Stroop test (CWST, the Visual Reasoning test and the Trail Making test (TMT were used to assess cognitive function. Results Multi-domain CogTr produced statistically significant training effects on RBANS, visual reasoning, and immediate and delayed memory, while single-domain CogTr showed training effects on RBANS, visual reasoning, word interference, and visuospatial/constructional score (all P Conclusions Cognitive training can improve memory, visual reasoning, visuospatial construction, attention and neuropsychological status in community-living older people and can help maintain their functioning over time. Multi-domain CogTr enhanced memory proficiency, while single-domain CogTr augmented visuospatial/constructional and attention abilities. Multi-domain CogTr had more advantages in training effect maintenance. Clinical Trial Registration Chinese Clinical Trial Registry. Registration number: ChiCTR-TRC-09000732.

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

    Czech Academy of Sciences Publication Activity Database

    Bureš, Zbyněk

    2012-01-01

    Roč. 106, č. 2 (2012), s. 111-122 ISSN 0340-1200 R&D Projects: GA ČR(CZ) GAP303/12/1347; GA ČR(CZ) GAP304/12/1342; GA ČR(CZ) GBP304/12/G069 Grant - others:GA MŠk(CZ) M00176 Institutional research plan: CEZ:AV0Z50390512 Institutional support: RVO:68378041 Keywords : aerosol * simulation of human breathing * porcine lung equivalent Subject RIV: ED - Physiology Impact factor: 2.067, year: 2012

  13. Estimating individual firing frequencies in a multiple spike train record

    Czech Academy of Sciences Publication Activity Database

    Pokora, Ondřej; Lánský, Petr

    2012-01-01

    Roč. 211, č. 2 (2012), s. 191-202 ISSN 0165-0270 R&D Projects: GA ČR(CZ) GAP103/11/0282; GA ČR(CZ) GBP304/12/G069 Institutional support: RVO:67985823 Keywords : firing rate * multi-unit recording Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.114, year: 2012

  14. Parameters of Spike Trains Observed in a Short Time Windows

    Czech Academy of Sciences Publication Activity Database

    Pawlas, Z.; Klebanov, L. B.; Prokop, M.; Lánský, Petr

    2008-01-01

    Roč. 20, č. 5 (2008), s. 1325-1343 ISSN 0899-7667 R&D Projects: GA AV ČR(CZ) IAA101120604; GA MŠk(CZ) LC554; GA AV ČR(CZ) 1ET400110401 Grant - others:GA ČR(CZ) GP201/06/P075 Institutional research plan: CEZ:AV0Z50110509 Keywords : action potential * estimation * stochastic point process Subject RIV: BO - Biophysics Impact factor: 2.378, year: 2008

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

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2018-01-01

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

  16. Macroscopic phase-resetting curves for spiking neural networks

    Science.gov (United States)

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

    2017-10-01

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

  17. The Mutation Frequency in Different Spike Categories in Barley

    DEFF Research Database (Denmark)

    Frydenberg, O.; Doll, Hans; Sandfær, J.

    1964-01-01

    After gamma irradiation of barley seeds, a comparison has been made between the chlorophyll-mutant frequencies in X1 spikes that had multicellular bud meristems in the seeds at the time of treatment (denoted as pre-formed spikes) and X1 spikes having no recognizable meristems at the time...

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

    Indian Academy of Sciences (India)

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

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

    Indian Academy of Sciences (India)

    58

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

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

    Science.gov (United States)

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

    2013-05-01

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

  1. Spiked instantons from intersecting D-branes

    Directory of Open Access Journals (Sweden)

    Nikita Nekrasov

    2017-01-01

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

  2. Non-singular spiked harmonic oscillator

    International Nuclear Information System (INIS)

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

    1990-01-01

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

  3. Basalt FRP Spike Repairing of Wood Beams

    Directory of Open Access Journals (Sweden)

    Luca Righetti

    2015-08-01

    Full Text Available This article describes aspects within an experimental program aimed at improving the structural performance of cracked solid fir-wood beams repaired with Basalt Fiber Reinforced Polymer (BFRP spikes. Fir wood is characterized by its low density, low compression strength, and high level of defects, and it is likely to distort when dried and tends to fail under tension due to the presence of cracks, knots, or grain deviation. The proposed repair technique consists of the insertion of BFRP spikes into timber beams to restore the continuity of cracked sections. The experimental efforts deal with the evaluation of the bending strength and deformation properties of 24 timber beams. An artificially simulated cracking was produced by cutting the wood beams in half or notching. The obtained results for the repaired beams were compared with those of solid undamaged and damaged beams, and increases of beam capacity, bending strength and of modulus of elasticity, and analysis of failure modes was discussed. For notched beams, the application of the BFRP spikes was able to restore the original bending capacity of undamaged beams, while only a small part of the original capacity was recovered for beams that were cut in half.

  4. Spiking Neural P Systems With Scheduled Synapses.

    Science.gov (United States)

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

    2017-12-01

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

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

    Science.gov (United States)

    Graupner, Michael; Wallisch, Pascal; Ostojic, Srdjan

    2016-11-02

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

  6. A Model of Fast Hebbian Spike Latency Normalization

    Directory of Open Access Journals (Sweden)

    Hafsteinn Einarsson

    2017-05-01

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

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

    Directory of Open Access Journals (Sweden)

    Dan F M Goodman

    2008-11-01

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

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

    Science.gov (United States)

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

    2018-05-11

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

  9. A Single Bout of High-Intensity Interval Training Reduces Awareness of Subsequent Hypoglycemia in Patients With Type 1 Diabetes.

    Science.gov (United States)

    Rooijackers, Hanne M; Wiegers, Evita C; van der Graaf, Marinette; Thijssen, Dick H; Kessels, Roy P C; Tack, Cees J; de Galan, Bastiaan E

    2017-07-01

    High-intensity interval training (HIIT) has gained increasing popularity in patients with diabetes. HIIT acutely increases plasma lactate levels. This may be important, since the administration of lactate during hypoglycemia suppresses symptoms and counterregulation while preserving cognitive function. We tested the hypothesis that, in the short term, HIIT reduces awareness of hypoglycemia and attenuates hypoglycemia-induced cognitive dysfunction. In a randomized crossover trial, patients with type 1 diabetes and normal awareness of hypoglycemia (NAH), patients with impaired awareness of hypoglycemia (IAH), and healthy participants ( n = 10 per group) underwent a hyperinsulinemic-hypoglycemic (2.6 mmol/L) clamp, either after a HIIT session or after seated rest. Compared with rest, HIIT reduced symptoms of hypoglycemia in patients with NAH but not in healthy participants or patients with IAH. HIIT attenuated hypoglycemia-induced cognitive dysfunction, which was mainly driven by changes in the NAH subgroup. HIIT suppressed cortisol and growth hormone responses, but not catecholamine responses to hypoglycemia. The present findings demonstrate that a single HIIT session rapidly reduces awareness of subsequent hypoglycemia in patients with type 1 diabetes and NAH, but does not in patients with IAH, and attenuates hypoglycemia-induced cognitive dysfunction. The role of exercise-induced lactate in mediating these effects, potentially serving as an alternative fuel for the brain, should be further explored. © 2017 by the American Diabetes Association.

  10. Cervical stability training with and without core stability training for patients with cervical disc herniation: A randomized, single-blind study.

    Science.gov (United States)

    Buyukturan, B; Guclu-Gunduz, A; Buyukturan, O; Dadali, Y; Bilgin, S; Kurt, E E

    2017-11-01

    This study aims at evaluating and comparing the effects of cervical stability training to combined cervical and core stability training in patients with neck pain and cervical disc herniation. Fifty patients with neck pain and cervical disc herniation were included in the study, randomly divided into two groups as cervical stability and cervical-core stability. Training was applied three times a week in three phases, and lasted for a total duration of 8 weeks. Pain, activation and static endurance of deep cervical flexor muscles, static endurance of neck muscles, cross-sectional diameter of M. Longus Colli, static endurance of trunk muscles, disability and kinesiophobia were assessed. Pain, activation and static endurance of deep cervical flexors, static endurance of neck muscles, cross-sectional diameter of M. Longus Colli, static endurance of trunk muscles, disability and kinesiophobia improved in both groups following the training sessions (p < 0.05). Comparison of the effectiveness of these two training methods revealed that the cervical stability group produced a greater increase in the right transverse diameter of M. Longus Colli (p < 0.05). However, static endurance of trunk muscles and kinesiophobia displayed better improvement in the cervical-core stability group (p < 0.05). Cervical stability training provided benefit to patients with cervical disc herniation. The addition of core stability training did not provide any additional significant benefit. Further research is required to investigate the efficacy of combining other techniques with cervical stability training in patients with cervical disc herniation. Both cervical stability training and its combination with core stability training were significantly and similarly effective on neck pain and neck muscle endurance in patients with cervical disc herniation. © 2017 European Pain Federation - EFIC®.

  11. Spike Timing Matters in Novel Neuronal Code Involved in Vibrotactile Frequency Perception.

    Science.gov (United States)

    Birznieks, Ingvars; Vickery, Richard M

    2017-05-22

    Skin vibrations sensed by tactile receptors contribute significantly to the perception of object properties during tactile exploration [1-4] and to sensorimotor control during object manipulation [5]. Sustained low-frequency skin vibration (sensation referred to as flutter whose frequency can be clearly perceived [6]. How afferent spiking activity translates into the perception of frequency is still unknown. Measures based on mean spike rates of neurons in the primary somatosensory cortex are sufficient to explain performance in some frequency discrimination tasks [7-11]; however, there is emerging evidence that stimuli can be distinguished based also on temporal features of neural activity [12, 13]. Our study's advance is to demonstrate that temporal features are fundamental for vibrotactile frequency perception. Pulsatile mechanical stimuli were used to elicit specified temporal spike train patterns in tactile afferents, and subsequently psychophysical methods were employed to characterize human frequency perception. Remarkably, the most salient temporal feature determining vibrotactile frequency was not the underlying periodicity but, rather, the duration of the silent gap between successive bursts of neural activity. This burst gap code for frequency represents a previously unknown form of neural coding in the tactile sensory system, which parallels auditory pitch perception mechanisms based on purely temporal information where longer inter-pulse intervals receive higher perceptual weights than short intervals [14]. Our study also demonstrates that human perception of stimuli can be determined exclusively by temporal features of spike trains independent of the mean spike rate and without contribution from population response factors. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. The TMS Motor Map does not change following a single session of mirror training either with or without motor imagery

    NARCIS (Netherlands)

    van de Ruit, M.L.; Grey, M.J.

    2017-01-01

    Both motor imagery and mirror training have been used in motor rehabilitation settings to promote skill learning and plasticity. As motor imagery and mirror training are suggested to be closely linked, it was hypothesized that mirror training augmented by motor imagery would increase corticospinal

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

    DEFF Research Database (Denmark)

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

    2006-01-01

    Electrokinetic remediation methods for removal of heavy metals from polluted soils have been subjected for quite intense research during the past years since these methods are well suitable for fine-grained soils where other remediation methods fail. Electrodialytic remediation is an electrokinetic...... remediation method which is based on applying an electric DC field and the use of ion exchange membranes that ensures the main transport of heavy metals to be out of the pollutes soil. An experimental investigation was made with electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially...... polluted soil under the same operational conditions (constant current density 0.2 mA/cm2 and duration 28 days). The results of the present paper show that caution must be taken when generalising results obtained in spiked kaolinite to remediation of industrially polluted soils, as it was shown...

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

    Directory of Open Access Journals (Sweden)

    Evangelos Stromatias

    2017-06-01

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

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

    African Journals Online (AJOL)

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

  16. Effects of jump and balance training on knee kinematics and electromyography of female basketball athletes during a single limb drop landing: pre-post intervention study

    Directory of Open Access Journals (Sweden)

    Nagano Yasuharu

    2011-07-01

    Full Text Available Abstract Background Some research studies have investigated the effects of anterior cruciate ligament (ACL injury prevention programs on knee kinematics during landing tasks; however the results were different among the studies. Even though tibial rotation is usually observed at the time of ACL injury, the effects of training programs for knee kinematics in the horizontal plane have not yet been analyzed. The purpose of this study was to determine the effects of a jump and balance training program on knee kinematics including tibial rotation as well as on electromyography of the quadriceps and hamstrings in female athletes. Methods Eight female basketball athletes participated in the experiment. All subjects performed a single limb landing at three different times: the initial test, five weeks later, and one week after completing training. The jump and balance training program lasted for five weeks. Knee kinematics and simultaneous electromyography of the rectus femoris and Hamstrings before training were compared with those measured after completing the training program. Results After training, regarding the position of the knee at foot contact, the knee flexion angle for the Post-training trial (mean (SE: 24.4 (2.1 deg was significantly larger than that for the Pre-training trial (19.3 (2.5 deg (p Conclusions The jump and balance training program successfully increased knee flexion and hamstring activity of female athletes during landing, and has the possibility of producing partial effects to avoid the characteristic knee position observed in ACL injury, thereby preventing injury. However, the expected changes in frontal and transverse kinematics of the knee were not observed.

  17. Feasibility of a Humor Training to Promote Humor and Decrease Stress in a Subclinical Sample: A Single-Arm Pilot Study

    Directory of Open Access Journals (Sweden)

    Nektaria Tagalidou

    2018-04-01

    Full Text Available The present study investigates the feasibility of a humor training for a subclinical sample suffering from increased stress, depressiveness, or anxiety. Based on diagnostic interviews, 35 people were invited to participate in a 7-week humor training. Evaluation measures were filled in prior training, after training, and at a 1-month follow-up including humor related outcomes (coping humor and cheerfulness and mental health-related outcomes (perceived stress, depressiveness, anxiety, and well-being. Outcomes were analyzed using repeated-measures ANOVAs. Within-group comparisons of intention-to-treat analysis showed main effects of time with large effect sizes on all outcomes. Post hoc tests showed medium to large effect sizes on all outcomes from pre to post and results remained stable until follow-up. Satisfaction with the training was high, attrition rate low (17.1%, and participants would highly recommend the training. Summarizing the results, the pilot study showed promising effects for people suffering from subclinical symptoms. All outcomes were positively influenced and showed stability over time. Humor trainings could be integrated more into mental health care as an innovative program to reduce stress whilst promoting also positive emotions. However, as this study was a single-arm pilot study, further research (including also randomized controlled trials is still needed to evaluate the effects more profoundly.

  18. Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

    Science.gov (United States)

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

    2016-01-01

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

  19. A new coding concept for fast ultrasound imaging using pulse trains

    DEFF Research Database (Denmark)

    Misaridis, T.; Jensen, Jørgen Arendt

    2002-01-01

    . In this paper, an alternative combined time-space coding approach is undertaken. In the new method all transducer elements are excited with short pulses and the high time-bandwidth (TB) product waveforms are generated acoustically. Each element transmits a short pulse spherical wave with a constant transmit...... delay from element to element, long enough to assure no pulse overlapping for all depths in the image. Frequency shift keying is used for "per element" coding. The received signals from a point scatterer are staggered pulse trains which are beamformed for all beam directions and further processed...... with a bank of matched filters (one for each beam direction). Filtering compresses the pulse train to a single pulse at the scatterer position with a number of spike axial sidelobes. Cancellation of the ambiguity spikes is done by applying additional phase modulation from one emission to the next and summing...

  20. From sensors to spikes: evolving receptive fields to enhance sensorimotor information in a robot-arm.

    Science.gov (United States)

    Luque, Niceto R; Garrido, Jesús A; Ralli, Jarno; Laredo, Juanlu J; Ros, Eduardo

    2012-08-01

    In biological systems, instead of actual encoders at different joints, proprioception signals are acquired through distributed receptive fields. In robotics, a single and accurate sensor output per link (encoder) is commonly used to track the position and the velocity. Interfacing bio-inspired control systems with spiking neural networks emulating the cerebellum with conventional robots is not a straight forward task. Therefore, it is necessary to adapt this one-dimensional measure (encoder output) into a multidimensional space (inputs for a spiking neural network) to connect, for instance, the spiking cerebellar architecture; i.e. a translation from an analog space into a distributed population coding in terms of spikes. This paper analyzes how evolved receptive fields (optimized towards information transmission) can efficiently generate a sensorimotor representation that facilitates its discrimination from other "sensorimotor states". This can be seen as an abstraction of the Cuneate Nucleus (CN) functionality in a robot-arm scenario. We model the CN as a spiking neuron population coding in time according to the response of mechanoreceptors during a multi-joint movement in a robot joint space. An encoding scheme that takes into account the relative spiking time of the signals propagating from peripheral nerve fibers to second-order somatosensory neurons is proposed. Due to the enormous number of possible encodings, we have applied an evolutionary algorithm to evolve the sensory receptive field representation from random to optimized encoding. Following the nature-inspired analogy, evolved configurations have shown to outperform simple hand-tuned configurations and other homogenized configurations based on the solution provided by the optimization engine (evolutionary algorithm). We have used artificial evolutionary engines as the optimization tool to circumvent nonlinearity responses in receptive fields.

  1. Interlaboratory comparison of PCR-based identification of Candida and Aspergillus DNA in spiked blood samples.

    Science.gov (United States)

    Reichard, Utz; Buchheidt, Dieter; Lass-Flörl, Cornelia; Loeffler, Juergen; Lugert, Raimond; Ruhnke, Markus; Tintelnot, Kathrin; Weig, Michael; Groß, Uwe

    2012-09-01

    Despite PCR per se being a powerful and sensitive technique, regarding the detection of fungi in patients' blood, no consensus for a standardised PCR protocol yet exists. To complement other ongoing or accomplished studies which tackle this problem, the German Reference Center for Systemic Mycoses conducted an interlaboratory comparison starting with blood samples spiked with fungal cell elements. Altogether, six laboratories using in-house PCR-protocols from Germany and Austria participated in the trial. Blood samples were spiked with vital cells of Candida albicans or Aspergillus fumigatus. Candida was used in the yeast form, whereas Aspergillus cells were either spiked as conidia or as very young germlings, also known as smoo cells. Spiked blood samples contained between 10 and 10 000 cells ml(-1). Depending on the techniques used for fungal cell disruption and DNA-amplification, detection quality was variable between laboratories, but also differed within single laboratories in different trials particularly for samples spiked with less than 100 cells ml(-1). Altogether, at least regarding the detection of A. fumigatus, two of six laboratories showed constant reliable test results also with low fungal cell number spiked samples. Protocols used by these labs do not differ substantially from others. However, as particularities, one protocol included a conventional phenol chloroform extraction during the DNA preparation process and the other included a real time PCR-protocol based on FRET probes. Other laboratory comparisons on the basis of clinical samples should follow to further evaluate the procedures. The difficulties and problems of such trials in general are discussed. © 2012 Blackwell Verlag GmbH.

  2. Target representation of naturalistic echolocation sequences in single unit responses from the inferior colliculus of big brown bats

    Science.gov (United States)

    Sanderson, Mark I.; Simmons, James A.

    2005-11-01

    Echolocating big brown bats (Eptesicus fuscus) emit trains of frequency-modulated (FM) biosonar signals whose duration, repetition rate, and sweep structure change systematically during interception of prey. When stimulated with a 2.5-s sequence of 54 FM pulse-echo pairs that mimic sounds received during search, approach, and terminal stages of pursuit, single neurons (N=116) in the bat's inferior colliculus (IC) register the occurrence of a pulse or echo with an average of <1 spike/sound. Individual IC neurons typically respond to only a segment of the search or approach stage of pursuit, with fewer neurons persisting to respond in the terminal stage. Composite peristimulus-time-histogram plots of responses assembled across the whole recorded population of IC neurons depict the delay of echoes and, hence, the existence and distance of the simulated biosonar target, entirely as on-response latencies distributed across time. Correlated changes in pulse duration, repetition rate, and pulse or echo amplitude do modulate the strength of responses (probability of the single spike actually occurring for each sound), but registration of the target itself remains confined exclusively to the latencies of single spikes across cells. Modeling of echo processing in FM biosonar should emphasize spike-time algorithms to explain the content of biosonar images.

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

    Science.gov (United States)

    Yoshioka, Masahiko

    2002-01-01

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

  4. Eliminating thermal violin spikes from LIGO noise

    Energy Technology Data Exchange (ETDEWEB)

    Santamore, D. H.; Levin, Yuri

    2001-08-15

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

  5. Eliminating thermal violin spikes from LIGO noise

    International Nuclear Information System (INIS)

    Santamore, D. H.; Levin, Yuri

    2001-01-01

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

  6. Phase Diagram of Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Hamed eSeyed-Allaei

    2015-03-01

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

  7. Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.

    Science.gov (United States)

    Franosch, Jan-Moritz P; Urban, Sebastian; van Hemmen, J Leo

    2013-12-01

    How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as "supervisor." Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

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

    Directory of Open Access Journals (Sweden)

    Daniel ede Santos-Sierra

    2015-11-01

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

  9. Comparison of forward versus backward walking using body weight supported treadmill training in an individual with a spinal cord injury: a single subject design.

    Science.gov (United States)

    Moriello, Gabriele; Pathare, Neeti; Cirone, Cono; Pastore, Danielle; Shears, Dacia; Sulehri, Sahira

    2014-01-01

    Body weight supported treadmill training (BWSTT) is a task-specific intervention that promotes functional locomotion. There is no research evaluating the effect of backward walking (BW) using BWSTT in individuals with spinal cord injury (SCI). The purpose of this single subject design was to examine the differences between forward walking (FW) and BW training using BWSTT in an individual with quadriparesis. The participant was a 57-year-old male with incomplete C3-C6 SCI. An ABABAB design (A = BW; B = FW; each phase = 3 weeks of biweekly sessions) was utilized. Outcome measures included: gait parameters; a timed 4-meter walk; the 5-repetition sit-to-stand test (STST); tandem stance time; and 6-minute walk test (6MWT). Data was analyzed with split level method of trend estimation. Improvements in gait parameters, on the timed 4-meter walk, 6MWT, tandem balance and aerobic endurance were similar with FW and BW training. The only difference between FW and BW training was that BW training resulted in greater improvements in the STST. The results of this study suggest that in this individual backward walking training was advantageous, resulting in improved ability to perform the 5-repetition STST. It is suspected that these changes can be attributed to the differences in muscle activation and task difficulty between FW and BW.

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

    Directory of Open Access Journals (Sweden)

    Drew Benjamin Sinha

    2014-01-01

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

  11. Spike-timing dependent plasticity and the cognitive map

    Directory of Open Access Journals (Sweden)

    Daniel eBush

    2010-10-01

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

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

    Science.gov (United States)

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

    2015-01-01

    Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time. PMID:26217169

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

    Science.gov (United States)

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

    2012-06-01

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

  14. Balance training improves postural balance, gait, and functional strength in adolescents with intellectual disabilities: Single-blinded, randomized clinical trial.

    Science.gov (United States)

    Lee, Kyeongjin; Lee, Myungmo; Song, Changho

    2016-07-01

    Adolescents with intellectual disabilities often present with problems of balance and mobility. Balance training is an important component of physical activity interventions, with growing evidence that it can be beneficial for people with intellectual disabilities. The aim of this study was to investigate the effect of balance training on postural balance, gait, and functional strength in adolescents with intellectual disabilities. Thirty-two adolescents with intellectual disabilities aged 14-19 years were randomly assigned either to a balance training group (n = 15) or a control group (n = 16). Subjects in the balance training group underwent balance training for 40 min per day, two times a week, for 8 weeks. All subjects were assessed with posture sway and the one-leg stance test for postural balance; the timed up-and-go test and 10-m walk test for gait; and sit to stand test for functional strength. Postural balance and functional strength showed significant improvements in the balance training group (p functional strength significantly improved in the balance training group compared with those in the control group. Balance training for adolescents with intellectual disabilities might be beneficial for improving postural balance and functional strength. Copyright © 2016 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    Ha, Go Eun; Cheong, Eunji

    2017-08-01

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

  16. Linear stability analysis of retrieval state in associative memory neural networks of spiking neurons

    Science.gov (United States)

    Yoshioka, Masahiko

    2002-12-01

    We study associative memory neural networks of the Hodgkin-Huxley type of spiking neurons in which multiple periodic spatiotemporal patterns of spike timing are memorized as limit-cycle-type attractors. In encoding the spatiotemporal patterns, we assume the spike-timing-dependent synaptic plasticity with the asymmetric time window. Analysis for periodic solution of retrieval state reveals that if the area of the negative part of the time window is equivalent to the positive part, then crosstalk among encoded patterns vanishes. Phase transition due to the loss of the stability of periodic solution is observed when we assume fast α function for direct interaction among neurons. In order to evaluate the critical point of this phase transition, we employ Floquet theory in which the stability problem of the infinite number of spiking neurons interacting with α function is reduced to the eigenvalue problem with the finite size of matrix. Numerical integration of the single-body dynamics yields the explicit value of the matrix, which enables us to determine the critical point of the phase transition with a high degree of precision.

  17. AMORE Mo-99 Spike Test Results

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-09-27

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

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

    Directory of Open Access Journals (Sweden)

    Henry C Tuckwell

    2010-05-01

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

  19. Receptive field optimisation and supervision of a fuzzy spiking neural network.

    Science.gov (United States)

    Glackin, Cornelius; Maguire, Liam; McDaid, Liam; Sayers, Heather

    2011-04-01

    This paper presents a supervised training algorithm that implements fuzzy reasoning on a spiking neural network. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train firing rates and behave in a similar manner as fuzzy membership functions. The connectivity of the hidden and output layers in the fuzzy spiking neural network (FSNN) is representative of a fuzzy rule base. Fuzzy C-Means clustering is utilised to produce clusters that represent the antecedent part of the fuzzy rule base that aid classification of the feature data. Suitable cluster widths are determined using two strategies; subjective thresholding and evolutionary thresholding respectively. The former technique typically results in compact solutions in terms of the number of neurons, and is shown to be particularly suited to small data sets. In the latter technique a pool of cluster candidates is generated using Fuzzy C-Means clustering and then a genetic algorithm is employed to select the most suitable clusters and to specify cluster widths. In both scenarios, the network is supervised but learning only occurs locally as in the biological case. The advantages and disadvantages of the network topology for the Fisher Iris and Wisconsin Breast Cancer benchmark classification tasks are demonstrated and directions of current and future work are discussed. Copyright © 2010 Elsevier Ltd. All rights reserved.

  20. 2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism.

    Science.gov (United States)

    Wu, QingXiang; McGinnity, Thomas Martin; Maguire, Liam; Belatreche, Ammar; Glackin, Brendan

    2008-11-01

    In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.

  1. Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks

    Directory of Open Access Journals (Sweden)

    Jarmo Partanen

    2013-11-01

    Full Text Available A forecasting methodology for prediction of both normal prices and price spikes in the day-ahead energy market is proposed. The method is based on an iterative strategy implemented as a combination of two modules separately applied for normal price and price spike predictions. The normal price module is a mixture of wavelet transform, linear AutoRegressive Integrated Moving Average (ARIMA and nonlinear neural network models. The probability of a price spike occurrence is produced by a compound classifier in which three single classification techniques are used jointly to make a decision. Combined with the spike value prediction technique, the output from the price spike module aims to provide a comprehensive price spike forecast. The overall electricity price forecast is formed as combined normal price and price spike forecasts. The forecast accuracy of the proposed method is evaluated with real data from the Finnish Nord Pool Spot day-ahead energy market. The proposed method provides significant improvement in both normal price and price spike prediction accuracy compared with some of the most popular forecast techniques applied for case studies of energy markets.

  2. Effectiveness of Parental Skills Training on Worry, Anxiety and Self-Efficacy Beliefs of Single-Child and Multi-Child Parents

    Directory of Open Access Journals (Sweden)

    A Hajigholami Yazdi

    2013-06-01

    Full Text Available Introduction: Each family utilizes specific methods for personal and social education of their children. These methods that are called “Parenting style” are affected by various factors such as biological, cultural, social, political, and economic factors. The present study intends to investigate the effectiveness of parental skills training on worry, anxiety and self-efficacy beliefs of single-child and multi-child parents. Methods: In this experimental study, two private girls' school located in the city of Karaj, were randomly selected as the control and experimental groups. Parents of experimental group’s students (54 couples with a voluntary assignment participated in 8 training sessions. Data were obtained by General Self-efficacy Beliefs Questionnaire, Beck Anxiety Inventory (BAI, Penn State Worry Questionnaire (PSWQ which were then analyzed by t-test and ANOVA. Results: Results showed that there was not any significant difference in the pretest between single-child and multi-child parents. Regarding control and experimental groups, a significant difference has been detected between the pretest and posttest between two groups. Multifactor ANOVA test results also showed that the effect of parental skills training is significant on fear, anxiety and self-efficacy. But the number of children does not have any significant effect on the fear, anxiety and self-efficacy. Conclusion: Findings emphasize the necessity and importance of parental skills training to facilitate children nurture, decrease stress and worry resulting from parenting responsibility.

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

    Science.gov (United States)

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

    2014-04-01

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

  4. A single bout of high-intensity interval training reduces awareness of subsequent hypoglycemia in patients with type 1 diabetes

    NARCIS (Netherlands)

    Rooijackers, H.M.M.; Wiegers, E.C.; Graaf, M. van der; Thijssen, D.H.J.; Kessels, R.P.C.; Tack, C.J.J.; Galan, B.E. de

    2017-01-01

    High-intensity interval training (HIIT) gains increasing popularity in patients with diabetes. HIIT acutely increases plasma lactate levels. This may be important, since administration of lactate during hypoglycemia suppresses symptoms and counterregulation, whilst preserving cognitive function. We

  5. Clustering predicts memory performance in networks of spiking and non-spiking neurons

    Directory of Open Access Journals (Sweden)

    Weiliang eChen

    2011-03-01

    Full Text Available The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

  6. Impairment-oriented training or Bobath therapy for severe arm paresis after stroke: a single-blind, multicentre randomized controlled trial.

    Science.gov (United States)

    Platz, T; Eickhof, C; van Kaick, S; Engel, U; Pinkowski, C; Kalok, S; Pause, M

    2005-10-01

    To study the effects of augmented exercise therapy time for arm rehabilitation as either Bobath therapy or the impairment-oriented training (Arm BASIS training) in stroke patients with arm severe paresis. Single blind, multicentre randomized control trial. Three inpatient neurorehabilitation centres. Sixty-two anterior circulation ischaemic stroke patients. Random assignment to three group: (A) no augmented exercise therapy time, (B) augmented exercise therapy time as Bobath therapy and (C) augmented exercise therapy time as Arm BASIS training. Fugl-Meyer arm motor score. Secondary measure: Action Research Arm Test (ARA). Ancillary measures: Fugl-Meyer arm sensation and joint motion/pain scores and the Ashworth Scale (elbow flexors). An overall effect of augmented exercise therapy time on Fugl-Meyer scores after four weeks was not corroborated (mean and 95% confidence interval (CI) of change scores: no augmented exercise therapy time (n=20) 8.8, 5.2-12.3; augmented exercise therapy time (n=40) 9.9, 6.8-13.9; p = 0.2657). The group who received the augmented exercise therapy time as Arm BASIS training (n=20) had, however, higher gains than the group receiving the augmented exercise therapy time as Bobath therapy (n=20) (mean and 95% CI of change scores: Bobath 7.2, 2.6-11.8; BASIS 12.6, 8.4-16.8; p = 0.0432). Passive joint motion/pain deteriorated less in the group who received BASIS training (mean and 95% CI of change scores: Bobath -3.2, -5.2 to -1.1; BASIS 0.1, -1.8-2.0; p = 0.0090). ARA, Fugl-Meyer arm sensation, and Ashworth Scale scores were not differentially affected. The augmented exercise therapy time as Arm BASIS training enhanced selective motor control. Type of training was more relevant for recovery of motor control than therapeutic time spent.

  7. Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm

    Science.gov (United States)

    Dura-Bernal, Salvador; Zhou, Xianlian; Neymotin, Samuel A.; Przekwas, Andrzej; Francis, Joseph T.; Lytton, William W.

    2015-01-01

    Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of

  8. Cortical spiking network interfaced with virtual musculoskeletal arm and robotic arm

    Directory of Open Access Journals (Sweden)

    Salvador eDura-Bernal

    2015-11-01

    Full Text Available Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm.This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuro-prosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility

  9. No WIMP mini-spikes in dwarf spheroidal galaxies

    NARCIS (Netherlands)

    Wanders, M.; Bertone, G.; Volonteri, M.; Weniger, C.

    2015-01-01

    The formation of black holes inevitably affects the distribution of dark and baryonic matter in their vicinity, leading to an enhancement of the dark matter density, called spike, and if dark matter is made of WIMPs, to a strong enhancement of the dark matter annihilation rate. Spikes at the center

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

    DEFF Research Database (Denmark)

    Boysen, Ole; Jensen, Hans Grinsted

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

  11. Spiking and bursting patterns of fractional-order Izhikevich model

    Science.gov (United States)

    Teka, Wondimu W.; Upadhyay, Ranjit Kumar; Mondal, Argha

    2018-03-01

    Bursting and spiking oscillations play major roles in processing and transmitting information in the brain through cortical neurons that respond differently to the same signal. These oscillations display complex dynamics that might be produced by using neuronal models and varying many model parameters. Recent studies have shown that models with fractional order can produce several types of history-dependent neuronal activities without the adjustment of several parameters. We studied the fractional-order Izhikevich model and analyzed different kinds of oscillations that emerge from the fractional dynamics. The model produces a wide range of neuronal spike responses, including regular spiking, fast spiking, intrinsic bursting, mixed mode oscillations, regular bursting and chattering, by adjusting only the fractional order. Both the active and silent phase of the burst increase when the fractional-order model further deviates from the classical model. For smaller fractional order, the model produces memory dependent spiking activity after the pulse signal turned off. This special spiking activity and other properties of the fractional-order model are caused by the memory trace that emerges from the fractional-order dynamics and integrates all the past activities of the neuron. On the network level, the response of the neuronal network shifts from random to scale-free spiking. Our results suggest that the complex dynamics of spiking and bursting can be the result of the long-term dependence and interaction of intracellular and extracellular ionic currents.

  12. Spike protection device for electronics and communication appliances

    African Journals Online (AJOL)

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

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

    Directory of Open Access Journals (Sweden)

    João Leodato Nunes Maciel

    2013-09-01

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

  14. Cytoplasmic tail of coronavirus spike protein has intracellular

    Indian Academy of Sciences (India)

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

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

    Science.gov (United States)

    Fernando, Chrisantha

    2011-04-21

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

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

    Science.gov (United States)

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

    2005-08-01

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

  17. Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance

    Science.gov (United States)

    Christie, Breanne P.; Tat, Derek M.; Irwin, Zachary T.; Gilja, Vikash; Nuyujukian, Paul; Foster, Justin D.; Ryu, Stephen I.; Shenoy, Krishna V.; Thompson, David E.; Chestek, Cynthia A.

    2015-02-01

    Objective. For intracortical brain-machine interfaces (BMIs), action potential voltage waveforms are often sorted to separate out individual neurons. If these neurons contain independent tuning information, this process could increase BMI performance. However, the sorting of action potentials (‘spikes’) requires high sampling rates and is computationally expensive. To explicitly define the difference between spike sorting and alternative methods, we quantified BMI decoder performance when using threshold-crossing events versus sorted action potentials. Approach. We used data sets from 58 experimental sessions from two rhesus macaques implanted with Utah arrays. Data were recorded while the animals performed a center-out reaching task with seven different angles. For spike sorting, neural signals were sorted into individual units by using a mixture of Gaussians to cluster the first four principal components of the waveforms. For thresholding events, spikes that simply crossed a set threshold were retained. We decoded the data offline using both a Naïve Bayes classifier for reaching direction and a linear regression to evaluate hand position. Main results. We found the highest performance for thresholding when placing a threshold between -3 and -4.5 × Vrms. Spike sorted data outperformed thresholded data for one animal but not the other. The mean Naïve Bayes classification accuracy for sorted data was 88.5% and changed by 5% on average when data were thresholded. The mean correlation coefficient for sorted data was 0.92, and changed by 0.015 on average when thresholded. Significance. For prosthetics applications, these results imply that when thresholding is used instead of spike sorting, only a small amount of performance may be lost. The utilization of threshold-crossing events may significantly extend the lifetime of a device because these events are often still detectable once single neurons are no longer isolated.

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

    Directory of Open Access Journals (Sweden)

    Tiago L Ribeiro

    2010-11-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

  20. Does gender impact on female doctors'experiences in the training and practice of surgery? A single centre study.

    Science.gov (United States)

    Umoetok, F; Van Wyk, J M; Madiba, T E

    2017-09-01

    Surgery has been identified as a male-dominated specialty in South Africa and abroad. This study explored how female registrars perceived the impact of gender on their training and practice of surgery. A self-administered questionnaire was used to explore whether females perceived any benefits to training in a male-dominated specialty, their choice of mentors and the challenges that they encountered during surgical training. Thirty-two female registrars participated in the study. The respondents were mainly South African (91%) and enrolled in seven surgical specialties. Twenty-seven (84%) respondents were satisfied with their training and skills development. Twenty-four (75%) respondents had a mentor from the department. Seventeen (53%) respondents perceived having received differential treatment due to their gender and 25 (78.2%) thought that the gender of their mentor did not impact on the quality of the guidance received in surgery. Challenges included physical threats to female respondents from patients and disrespect, emotional threats and defaming statements from male registrars. Additional challenges included time-constraints for family and academic work, poor work-life balance and being treated differently due to their gender. Seventeen (53%) respondents would consider teaching in the Department of Surgery. Generally, females had positive perceptions of their training in Surgery. They expressed concern about finding and maintaining a work-life balance. The gender of their mentor did not impact on the quality of the training but 'bullying' from male peers and selected supervisors occurred. Respondents will continue to recommend the specialty as a satisfying career to young female students.

  1. The Effect of a Single Session of Whole-Body Vibration Training in Recreationally Active Men on the Excitability of the Central and Peripheral Nervous System

    Directory of Open Access Journals (Sweden)

    Chmielewska Daria

    2014-07-01

    Full Text Available Vibration training has become a popular method used in professional sports and recreation. In this study, we examined the effect of whole-body vibration training on the central nervous system and muscle excitability in a group of 28 active men. Subjects were assigned randomly to one of two experimental groups with different variables of vibrations. The chronaximetry method was used to evaluate the effect of a single session of whole-body vibration training on the excitability of the rectus femoris and brachioradialis muscles. The examination of the fusing and flickering frequencies of the light stimulus was performed. An increase in the excitability of the quadriceps femoris muscle due to low intensity vibrations (20 Hz frequency, 2 mm amplitude was noted, and a return to the initial values was observed 30 min after the application of vibration. High intensity vibrations (60 Hz frequency, 4 mm amplitude caused elongations of the chronaxy time; however, these differences were not statistically significant. Neither a low intensity vibration amplitude of 2 mm (frequency of 20 Hz nor a high intensity vibration amplitude of 4 mm (frequency of 60 Hz caused a change in the excitability of the central nervous system, as revealed by the average frequency of the fusing and flickering of the light stimulus. A single session of high intensity whole-body vibration did not significantly decrease the excitability of the peripheral nervous system while the central nervous system did not seem to be affected.

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

    Science.gov (United States)

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

    2006-07-01

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

  3. A single session of perturbation-based gait training with the A-TPAD improves dynamic stability in healthy young subjects.

    Science.gov (United States)

    Martelli, Dario; Kang, Jiyeon; Agrawal, Sunil K

    2017-07-01

    Gait and balance disorders are among the most common causes of falls in older adults. Most falls occur as a result of unexpected hazards while walking. In order to improve the effectiveness of current fall-prevention programs, new balance training paradigms aim to strengthen the control of the compensatory responses required after external perturbations. The aim of this study was to analyze the adaptions of reactive and proactive strategies to control stability after repeated exposures to waist-pull perturbations delivered while walking. Eight healthy young subjects participated in a single training session with the Active Tethered Pelvic assisted Device (A-TPAD). Participants were exposed to repeated multi-directional perturbations of increasing intensity. The Antero-Posterior (AP) and Medio-Lateral (ML) Base of Support (BoS) and Margin of Stability (MoS) during the response to diagonal perturbations were compared before and after the training. Results showed that participants adapted both the reactive and proactive strategies to control walking balance by significantly increasing their pre- and post-perturbation stability. The changes were principally accounted for by an increment of the AP BoS and MoS and a reduction of ML BoS. This improved their ability to react to a diagonal perturbation. We envision that this system can be used to develop a perturbation-based gait training aimed at improving balance and control of stability during walking, thus reducing fall risk.

  4. Uroguanylin induces electroencephalographic spikes in rats

    Directory of Open Access Journals (Sweden)

    MDA. Teixeira

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

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

    Science.gov (United States)

    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.

  6. Epileptic encephalopathy with continuous spike and wave during sleep associated to periventricular leukomalacia.

    Science.gov (United States)

    De Grandis, Elisa; Mancardi, Maria Margherita; Carelli, Valentina; Carpaneto, Manuela; Morana, Giovanni; Prato, Giulia; Mirabelli-Badenier, Marisol; Pinto, Francesca; Veneselli, Edvige; Baglietto, Maria Giuseppina

    2014-11-01

    Periventricular leukomalacia is the most common type of brain injury in premature infants. Our aim is to describe the frequency and the features of epilepsy in a single-center population of 137 children with periventricular leukomalacia. Forty-two of the 137 (31%) patients presented epilepsy. Twelve percent of these patients presented West syndrome, whereas 19% showed a pattern of continuous spike-waves during slow sleep syndrome. In the latter group, outcome was frequently unfavorable, with a greater number of seizures and more drug resistance. A significant association was found between epilepsy and neonatal seizures, spastic tetraplegia, and mental retardation. Although less common than in other forms of brain injury, epilepsy is nevertheless a significant complication in children with periventricular leukomalacia. The fairly frequent association with continuous spike-waves during slow sleep syndrome deserves particular attention: electroencephalographic sleep monitoring is important in order to provide early treatment and prevent further neurologic deterioration. © The Author(s) 2013.

  7. Striatal fast-spiking interneurons selectively modulate circuit output and are required for habitual behavior.

    Science.gov (United States)

    O'Hare, Justin K; Li, Haofang; Kim, Namsoo; Gaidis, Erin; Ade, Kristen; Beck, Jeff; Yin, Henry; Calakos, Nicole

    2017-09-05

    Habit formation is a behavioral adaptation that automates routine actions. Habitual behavior correlates with broad reconfigurations of dorsolateral striatal (DLS) circuit properties that increase gain and shift pathway timing. The mechanism(s) for these circuit adaptations are unknown and could be responsible for habitual behavior. Here we find that a single class of interneuron, fast-spiking interneurons (FSIs), modulates all of these habit-predictive properties. Consistent with a role in habits, FSIs are more excitable in habitual mice compared to goal-directed and acute chemogenetic inhibition of FSIs in DLS prevents the expression of habitual lever pressing. In vivo recordings further reveal a previously unappreciated selective modulation of SPNs based on their firing patterns; FSIs inhibit most SPNs but paradoxically promote the activity of a subset displaying high fractions of gamma-frequency spiking. These results establish a microcircuit mechanism for habits and provide a new example of how interneurons mediate experience-dependent behavior.

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

    Directory of Open Access Journals (Sweden)

    Adam Sol Shai

    2014-08-01

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

  9. Reduction of B1 sensitivity in selective single-slab 3D turbo spin echo imaging with very long echo trains.

    Science.gov (United States)

    Park, Jaeseok; Mugler, John P; Hughes, Timothy

    2009-10-01

    Single-slab 3D turbo/fast spin echo (SE) imaging with very long echo trains was recently introduced with slab selection using a highly selective excitation pulse and short, nonselective refocusing pulses with variable flip angles for high imaging efficiency. This technique, however, is vulnerable to image degradation in the presence of spatially varying B(1) amplitudes. In this work we develop a B(1) inhomogeneity-reduced version of single-slab 3D turbo/fast SE imaging based on the hypothesis that it is critical to achieve spatially uniform excitation. Slab selection was performed using composite adiabatic selective excitation wherein magnetization is tipped into the transverse plane by a nonselective adiabatic-half-passage pulse and then slab is selected by a pair of selective adiabatic-full-passage pulses. Simulations and experiments were performed to evaluate the proposed technique and demonstrated that this approach is a simple and efficient way to reduce B(1) sensitivity in single-slab 3D turbo/fast SE imaging with very long echo trains. (c) 2009 Wiley-Liss, Inc.

  10. Action potential propagation recorded from single axonal arbors using multi-electrode arrays.

    Science.gov (United States)

    Tovar, Kenneth R; Bridges, Daniel C; Wu, Bian; Randall, Connor; Audouard, Morgane; Jang, Jiwon; Hansma, Paul K; Kosik, Kenneth S

    2018-04-11

    We report the presence of co-occurring extracellular action potentials (eAPs) from cultured mouse hippocampal neurons among groups of planar electrodes on multi-electrode arrays (MEAs). The invariant sequences of eAPs among co-active electrode groups, repeated co-occurrences and short inter-electrode latencies are consistent with action potential propagation in unmyelinated axons. Repeated eAP co-detection by multiple electrodes was widespread in all our data records. Co-detection of eAPs confirms they result from the same neuron and allows these eAPs to be isolated from all other spikes independently of spike sorting algorithms. We averaged co-occurring events and revealed additional electrodes with eAPs that would otherwise be below detection threshold. We used these eAP cohorts to explore the temperature sensitivity of action potential propagation and the relationship between voltage-gated sodium channel density and propagation velocity. The sequence of eAPs among co-active electrodes 'fingerprints' neurons giving rise to these events and identifies them within neuronal ensembles. We used this property and the non-invasive nature of extracellular recording to monitor changes in excitability at multiple points in single axonal arbors simultaneously over several hours, demonstrating independence of axonal segments. Over several weeks, we recorded changes in inter-electrode propagation latencies and ongoing changes in excitability in different regions of single axonal arbors. Our work illustrates how repeated eAP co-occurrences can be used to extract physiological data from single axons with low electrode density MEAs. However, repeated eAP co-occurrences leads to over-sampling spikes from single neurons and thus can confound traditional spike-train analysis.

  11. Automated spike preparation system for Isotope Dilution Mass Spectrometry (IDMS)

    International Nuclear Information System (INIS)

    Maxwell, S.L. III; Clark, J.P.

    1990-01-01

    Isotope Dilution Mass Spectrometry (IDMS) is a method frequently employed to measure dissolved, irradiated nuclear materials. A known quantity of a unique isotope of the element to be measured (referred to as the ''spike'') is added to the solution containing the analyte. The resulting solution is chemically purified then analyzed by mass spectrometry. By measuring the magnitude of the response for each isotope and the response for the ''unique spike'' then relating this to the known quantity of the ''spike'', the quantity of the nuclear material can be determined. An automated spike preparation system was developed at the Savannah River Site (SRS) to dispense spikes for use in IDMS analytical methods. Prior to this development, technicians weighed each individual spike manually to achieve the accuracy required. This procedure was time-consuming and subjected the master stock solution to evaporation. The new system employs a high precision SMI Model 300 Unipump dispenser interfaced with an electronic balance and a portable Epson HX-20 notebook computer to automate spike preparation

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

    Science.gov (United States)

    Rangan, Aaditya V; Young, Lai-Sang

    2013-06-01

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

  13. Dynamic finite size effects in spiking neural networks.

    Directory of Open Access Journals (Sweden)

    Michael A Buice

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

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

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  15. An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

    Directory of Open Access Journals (Sweden)

    Huan-Yuan Chen

    2017-09-01

    Full Text Available This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

  16. DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

    Science.gov (United States)

    Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

    2015-12-01

    Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

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

    Science.gov (United States)

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

    2009-05-01

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

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

    DEFF Research Database (Denmark)

    Boysen, Ole; Jensen, Hans Grinsted

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

  19. Boobs, Boxing, and Bombs: Problematizing the Entertainment of Spike TV

    OpenAIRE

    Walton, Gerald; Potvin, L.

    2009-01-01

    Spike is the only television network in North America “for men.” Its motto, “Get more action,” is suggestive of pursuits of various forms of violence. We conceptualize Spike not as trivial entertainment, but rather as a form of pop culture that erodes the gains of feminists who have challenged the prevalence of normalized hegemonic masculinity (HM). Our paper highlights themes of Spike content, and connects those themes to the literature on HM. Moreover, we validate the identities and lives ...

  20. Characteristics of single large-conductance Ca2+-activated K+ channels and their regulation of action potentials and excitability in parasympathetic cardiac motoneurons in the nucleus ambiguus.

    Science.gov (United States)

    Lin, Min; Hatcher, Jeff T; Wurster, Robert D; Chen, Qin-Hui; Cheng, Zixi Jack

    2014-01-15

    Large-conductance Ca2(+)-activated K+ channels (BK) regulate action potential (AP) properties and excitability in many central neurons. However, the properties and functional roles of BK channels in parasympathetic cardiac motoneurons (PCMNs) in the nucleus ambiguus (NA) have not yet been well characterized. In this study, the tracer X-rhodamine-5 (and 6)-isothiocyanate (XRITC) was injected into the pericardial sac to retrogradely label PCMNs in FVB mice at postnatal 7-9 days. Two days later, XRITC-labeled PCMNs in brain stem slices were identified. Using excised patch single-channel recordings, we identified voltage-gated and Ca(2+)-dependent BK channels in PCMNs. The majority of BK channels exhibited persistent channel opening during voltage holding. These BK channels had a conductance of 237 pS and a 50% opening probability at +27.9 mV, the channel open time constant was 3.37 ms at +20 mV, and dwell time increased exponentially as the membrane potential depolarized. At the +20-mV holding potential, the [Ca2+]50 was 15.2 μM with a P0.5 of 0.4. Occasionally, some BK channels showed a transient channel opening and fast inactivation. Using whole cell voltage clamp, we found that BK channel mediated outward currents and afterhyperpolarization currents (IAHP). Using whole cell current clamp, we found that application of BK channel blocker iberiotoxin (IBTX) increased spike half-width and suppressed fast afterhyperpolarization (fAHP) amplitude following single APs. In addition, IBTX application increased spike half-width and reduced the spike frequency-dependent AP broadening in trains and spike frequency adaption (SFA). Furthermore, BK channel blockade decreased spike frequency. Collectively, these results demonstrate that PCMNs have BK channels that significantly regulate AP repolarization, fAHP, SFA, and spike frequency. We conclude that activation of BK channels underlies one of the mechanisms for facilitation of PCMN excitability.

  1. Markers of biological stress in response to a single session of high-intensity interval training and high-volume training in young athletes.

    Science.gov (United States)

    Kilian, Yvonne; Engel, Florian; Wahl, Patrick; Achtzehn, Silvia; Sperlich, Billy; Mester, Joachim

    2016-12-01

    The aim of the present study was to compare the effects of high-intensity interval training (HIIT) vs high-volume training (HVT) on salivary stress markers [cortisol (sC), testosterone (sT), alpha-amylase (sAA)], metabolic and cardiorespiratory response in young athletes. Twelve young male cyclists (14 ± 1 years; 57.9 ± 9.4 mL min -1  kg -1 peak oxygen uptake) performed one session of HIIT (4 × 4 min intervals at 90-95 % peak power output separated by 3 min of active rest) and one session of HVT (90 min constant load at 60 % peak power output). The levels of sC, sT, their ratio (sT/sC) and sAA were determined before and 0, 30, 60, 180 min after each intervention. Metabolic and cardiorespiratory stress was characterized by blood lactate, blood pH, respiratory exchange ratio (RER) and heart rate (HR), oxygen uptake ([Formula: see text]), ventilation (V E ) and ventilatory equivalent (V E /[Formula: see text]). sC increased 30 and 60 min after HIIT. However, 180 min post exercise, sC decreased below baseline levels in both conditions. sT increased 0 and 30 min after HIIT and 0 min after HVT. sAA and sT/sC ratio did not change significantly over time in HIIT nor HVT. Metabolic and cardiorespiratory stress, evidenced by blood lactate, HR, [Formula: see text], V E , and V E /[Formula: see text] was higher during HIIT compared to HVT. The metabolic and cardiorespiratory stress during HIIT was higher compared to HVT, but based on salivary analyses (cortisol, testosterone, alpha-amylase), we conclude no strong acute catabolic effects neither by HIIT nor by HVT.

  2. Does cognition-specific computer training have better clinical outcomes than non-specific computer training? A single-blind, randomized controlled trial.

    Science.gov (United States)

    Park, Ji-Hyuk; Park, Jin-Hyuck

    2018-02-01

    The purpose of this study was to investigate differences between non-specific computer training (NCT) and cognition-specific computer training (CCT). Randomized controlled experimental study. Local community welfare center. A total of 78 subjects with mild cognitive impairment (MCI) were randomly assigned to the NCT ( n = 39) or CCT group ( n = 39). The NCT group underwent NCT using Nintendo Wii for improving functional performance, while the CCT group underwent CCT using CoTras for improving function of the cognitive domain specifically. Subjects in both groups received 30-minute intervention three times a week for 10 weeks. To identify effects on cognitive function, the Wechsler Adult Intelligence Scale (WAIS) digit span subtests, Rey Auditory Verbal Learning Test (RAVLT), Trail Making Test-Part B (TMT-B), Rey-Osterrieth Complex Figure Test, and Modified Taylor Complex Figure (MTCF) were used. Health-related quality of life (HRQoL) was assessed using the Short-Form 36-item questionnaire. After 10 weeks, the WAIS subtests (digit span forward: 0.48 ± 0.08 vs. 0.12 ± 0.04; digit span backward: 0.46 ± 0.09 vs. 0.11 ± 0.04) and HRQoL (vitality: 9.05 ± 1.17 vs. 2.69 ± 1.67; role-emotional: 8.31 ± 1.20 vs. 4.15 ± 0.71; mental health: 11.62 ± 1.63 vs. 6.95 ± 1.75; bodily pain: 4.21 ± 2.17 vs. 0.10 ± 0.38) were significantly higher in the NCT group ( P < 0.05). NCT was superior to CCT for improving cognitive function and HRQoL of elderly adults with MCI.

  3. [Influence of D genome of wheat on expression of novel type spike branching in hybrid populations of 171ACS line].

    Science.gov (United States)

    Alieva, A J; Aminov, N Kh

    2013-11-01

    A 171ACS line (AABBDD, 2n = 6x = 42) has been crossed with the tetra- (AABB and AAGG, 2n = 4x = 28) and octoploid (AAAABBGG, 2n = 8x = 56) wheat species without the D genome, as well as with hexaploid (AABBDD and AAGGDD, 2n = 6x = 42) wheat species and tetra- (AADD, 2n = 4x = 28) and hexaploid (AADDSS, 2n = 6x = 42) amphidiploids that have the D genome. The inheritance of a novel type of spike branching in these obtained hybrid populations F1-F3 was studied. According to the results of a morphogenetic analysis of hybrid populations derived from crossings between 171ACS and wheat species without the D genome, the novel type of branching was found to be controlled by a single recessive gene (although a phenotype of the 171ACS line gives a handle for a doubt about occurrence of the second gene) and the 171ACS line is a source of gene of the novel type branching. However, not a single branched spike plant was observed in hybrid populations that were produced by crosses of the 171ACS line with wheat species, as well as with amphidiploids that have the D genome. This result also experimentally confirmed the inhibitor effect of chromosomes of the D genome on the expression of the spike-branching trait. The appearance of branched spike forms, together with normal spiked plants in hybrid populations of the 171ACS line and T. araraticum Jakubz. (AAGG) or T. fungicidum Zhuk. (AAAABBGG) confirmed that, as opposed to the D genome, neither genome G nor genome B demonstrated the inhibition of the expression of the spike-branching trait. In conclusion, keeping in mind that branching is exhibited in hybrid progenies obtained from crosses between the 171ACS line and wheat species with AABB and AAGG genomes, it can be said that this gene belongs to the A genome.

  4. Comparison of Listeria monocytogenes recoveries from spiked mung bean sprouts by the enrichment methods of three regulatory agencies.

    Science.gov (United States)

    Cauchon, Kaitlin E; Hitchins, Anthony D; Smiley, R Derike

    2017-09-01

    Three selective enrichment methods, the United States Food and Drug Administration's (FDA method), the United States Department of Agriculture Food Safety Inspection Service's (USDA method), and the EN ISO 11290-1 standard method, were assessed for their suitability for recovery of Listeria monocytogenes from spiked mung bean sprouts. Three parameters were evaluated; the enrichment L. monocytogenes population from singly-spiked sprouts, the enrichment L. monocytogenes population from doubly-spiked (L. monocytogenes and Listeria innocua) sprouts, and the population differential resulting from the enrichment of doubly-spiked sprouts. Considerable L. monocytogenes inter-strain variation was observed. The mean enrichment L. monocytogenes populations for singly-spiked sprouts were 6.1 ± 1.2, 4.9 ± 1.2, and 6.9 ± 2.3 log CFU/mL for the FDA, USDA, and EN ISO 11290-1 methods, respectively. The mean L. monocytogenes populations for doubly-spiked sprouts were 4.7 ± 1.1, 5.5 ± 1.3, and 4.6 ± 1.4 log CFU/mL for the FDA, USDA, and ISO 11290-1 enrichment methods, respectively. The corresponding mean population differentials were 2.8 ± 1.1, 3.3 ± 1.3, and 3.6 ± 1.4 Δlog CFU/mL for the same three enrichment methods, respectively. The presence of L. innocua and resident microorganisms on the sprouts negatively impacted final levels of L. monocytogenes with all three enrichment methods. Published by Elsevier Ltd.

  5. A single bout of high-intensity interval training improves motor skill retention in individuals with stroke

    DEFF Research Database (Denmark)

    Nepveu, Jean-Francois; Thiel, Alexander; Tang, Ada

    2017-01-01

    BACKGROUND: One bout of high-intensity cardiovascular exercise performed immediately after practicing a motor skill promotes changes in the neuroplasticity of the motor cortex and facilitates motor learning in nondisabled individuals. OBJECTIVE: To determine if a bout of exercise performed at high...... a motor task, the exercise group performed 15 minutes of high-intensity interval training while the control group rested. Twenty-four hours after motor practice all participants completed a test of the motor task to assess skill retention. RESULTS: The graded exercise test reduced interhemispheric...... imbalances in GABAA-mediated short-interval intracortical inhibition but changes in other markers of excitability were not statistically significant. The group that performed high-intensity interval training showed a better retention of the motor skill. CONCLUSIONS: The performance of a maximal graded...

  6. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  7. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

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

    Directory of Open Access Journals (Sweden)

    Javier A Caballero

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

  9. Assessment of long-term knowledge retention following single-day simulation training for uncommon but critical obstetrical events.

    Science.gov (United States)

    Vadnais, Mary A; Dodge, Laura E; Awtrey, Christopher S; Ricciotti, Hope A; Golen, Toni H; Hacker, Michele R

    2012-09-01

    The objectives were to determine (i) whether simulation training results in short-term and long-term improvement in the management of uncommon but critical obstetrical events and (ii) to determine whether there was additional benefit from annual exposure to the workshop. Physicians completed a pretest to measure knowledge and confidence in the management of eclampsia, shoulder dystocia, postpartum hemorrhage and vacuum-assisted vaginal delivery. They then attended a simulation workshop and immediately completed a posttest. Residents completed the same posttests 4 and 12 months later, and attending physicians completed the posttest at 12 months. Physicians participated in the same simulation workshop 1 year later and then completed a final posttest. Scores were compared using paired t-tests. Physicians demonstrated improved knowledge and comfort immediately after simulation. Residents maintained this improvement at 1 year. Attending physicians remained more comfortable managing these scenarios up to 1 year later; however, knowledge retention diminished with time. Repeating the simulation after 1 year brought additional improvement to physicians. Simulation training can result in short-term and contribute to long-term improvement in objective measures of knowledge and comfort level in managing uncommon but critical obstetrical events. Repeat exposure to simulation training after 1 year can yield additional benefits.

  10. Effects of intensive arm training with the rehabilitation robot ARMin II in chronic stroke patients: four single-cases

    Directory of Open Access Journals (Sweden)

    Nef Tobias

    2009-12-01

    Full Text Available Abstract Background Robot-assisted therapy offers a promising approach to neurorehabilitation, particularly for severely to moderately impaired stroke patients. The objective of this study was to investigate the effects of intensive arm training on motor performance in four chronic stroke patients using the robot ARMin II. Methods ARMin II is an exoskeleton robot with six degrees of freedom (DOF moving shoulder, elbow and wrist joints. Four volunteers with chronic (≥ 12 months post-stroke left side hemi-paresis and different levels of motor severity were enrolled in the study. They received robot-assisted therapy over a period of eight weeks, three to four therapy sessions per week, each session of one hour. Patients 1 and 4 had four one-hour training sessions per week and patients 2 and 3 had three one-hour training sessions per week. Primary outcome variable was the Fugl-Meyer Score of the upper extremity Assessment (FMA, secondary outcomes were the Wolf Motor Function Test (WMFT, the Catherine Bergego Scale (CBS, the Maximal Voluntary Torques (MVTs and a questionnaire about ADL-tasks, progress, changes, motivation etc. Results Three out of four patients showed significant improvements (p Conclusion Data clearly indicate that intensive arm therapy with the robot ARMin II can significantly improve motor function of the paretic arm in some stroke patients, even those in a chronic state. The findings of the study provide a basis for a subsequent controlled randomized clinical trial.

  11. Effects of intensive arm training with the rehabilitation robot ARMin II in chronic stroke patients: four single-cases

    Science.gov (United States)

    2009-01-01

    Background Robot-assisted therapy offers a promising approach to neurorehabilitation, particularly for severely to moderately impaired stroke patients. The objective of this study was to investigate the effects of intensive arm training on motor performance in four chronic stroke patients using the robot ARMin II. Methods ARMin II is an exoskeleton robot with six degrees of freedom (DOF) moving shoulder, elbow and wrist joints. Four volunteers with chronic (≥ 12 months post-stroke) left side hemi-paresis and different levels of motor severity were enrolled in the study. They received robot-assisted therapy over a period of eight weeks, three to four therapy sessions per week, each session of one hour. Patients 1 and 4 had four one-hour training sessions per week and patients 2 and 3 had three one-hour training sessions per week. Primary outcome variable was the Fugl-Meyer Score of the upper extremity Assessment (FMA), secondary outcomes were the Wolf Motor Function Test (WMFT), the Catherine Bergego Scale (CBS), the Maximal Voluntary Torques (MVTs) and a questionnaire about ADL-tasks, progress, changes, motivation etc. Results Three out of four patients showed significant improvements (p robot ARMin II can significantly improve motor function of the paretic arm in some stroke patients, even those in a chronic state. The findings of the study provide a basis for a subsequent controlled randomized clinical trial. PMID:20017939

  12. Multi-Tasking and Choice of Training Data Influencing Parietal ERP Expression and Single-Trial Detection—Relevance for Neuroscience and Clinical Applications

    Science.gov (United States)

    Kirchner, Elsa A.; Kim, Su Kyoung

    2018-01-01

    Event-related potentials (ERPs) are often used in brain-computer interfaces (BCIs) for communication or system control for enhancing or regaining control for motor-disabled persons. Especially results from single-trial EEG classification approaches for BCIs support correlations between single-trial ERP detection performance and ERP expression. Hence, BCIs can be considered as a paradigm shift contributing to new methods with strong influence on both neuroscience and clinical applications. Here, we investigate the relevance of the choice of training data and classifier transfer for the interpretability of results from single-trial ERP detection. In our experiments, subjects performed a visual-motor oddball task with motor-task relevant infrequent (targets), motor-task irrelevant infrequent (deviants), and motor-task irrelevant frequent (standards) stimuli. Under dual-task condition, a secondary senso-motor task was performed, compared to the simple-task condition. For evaluation, average ERP analysis and single-trial detection analysis with different numbers of electrodes were performed. Further, classifier transfer was investigated between simple and dual task. Parietal positive ERPs evoked by target stimuli (but not by deviants) were expressed stronger under dual-task condition, which is discussed as an increase of task emphasis and brain processes involved in task coordination and change of task set. Highest classification performance was found for targets irrespective whether all 62, 6 or 2 parietal electrodes were used. Further, higher detection performance of targets compared to standards was achieved under dual-task compared to simple-task condition in case of training on data from 2 parietal electrodes corresponding to results of ERP average analysis. Classifier transfer between tasks improves classification performance in case that training took place on more varying examples (from dual task). In summary, we showed that P300 and overlaying parietal positive

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

    Science.gov (United States)

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

    2017-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Gordon ePipa

    2011-06-01

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

  15. Sonar target localization based on spike coded spectrograms

    Directory of Open Access Journals (Sweden)

    Bertrand FONTAINE

    2006-12-01

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

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

    OpenAIRE

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

    2008-01-01

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

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

    OpenAIRE

    E. D. Belousova

    2012-01-01

    The author represents the review and discussion of current scientific literature devoted to epileptic encephalopathy with continuous spikes-waves activity during sleep — the special form of partly reversible age-dependent epileptic encephalopathy, characterized by triad of symptoms: continuous prolonged epileptiform (spike-wave) activity on EEG in sleep, epileptic seizures and cognitive disorders. The author describes the aspects of classification, pathogenesis and etiology, prevalence, clini...

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

    Science.gov (United States)

    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.

  19. Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment

    Science.gov (United States)

    Legenstein, Robert; Maass, Wolfgang

    2014-01-01

    It has recently been shown that networks of spiking neurons with noise can emulate simple forms of probabilistic inference through “neural sampling”, i.e., by treating spikes as samples from a probability distribution of network states that is encoded in the network. Deficiencies of the existing model are its reliance on single neurons for sampling from each random variable, and the resulting limitation in representing quickly varying probabilistic information. We show that both deficiencies can be overcome by moving to a biologically more realistic encoding of each salient random variable through the stochastic firing activity of an ensemble of neurons. The resulting model demonstrates that networks of spiking neurons with noise can easily track and carry out basic computational operations on rapidly varying probability distributions, such as the odds of getting rewarded for a specific behavior. We demonstrate the viability of this new approach towards neural coding and computation, which makes use of the inherent parallelism of generic neural circuits, by showing that this model can explain experimentally observed firing activity of cortical neurons for a variety of tasks that require rapid temporal integration of sensory information. PMID:25340749

  20. The activity requirements for spike timing-dependent plasticity in the hippocampus

    Directory of Open Access Journals (Sweden)

    Katherine Buchanan

    2010-06-01

    Full Text Available Synaptic plasticity has historically been investigated most intensely in the hippocampus and therefore it is somewhat surprising that the majority of studies on spike timing-dependent plasticity (STDP have focused not in the hippocampus but on synapses in the cortex. One of the major reasons for this bias is the relative ease in obtaining paired electrophysiological recordings from synaptically coupled neurons in cortical slices, in comparison to hippocampal slices. Another less obvious reason has been the difficulty in achieving reliable STDP in the hippocampal slice preparation and confusion surrounding the conditions required. The original descriptions of STDP in the hippocampus was performed on paired recordings from neurons in dissociated or slice cultures utilising single pairs of presynaptic and postsynaptic spikes and were subsequently replicated in acute hippocampal slices. Further work in several laboratories using conditions that more closely replicate the situation in vivo revealed a requirement for multiple postsynaptic spikes that necessarily complicate the absolute timing rules for STDP. Here we review the hippocampal STDP literature focusing on data from acute hippocampal slice preparations and highlighting apparently contradictory results and the variations in experimental conditions that might account for the discrepancies. We conclude by relating the majority of the available experimental data to a model for STDP induction in the hippocampus based on a critical role for postsynaptic Ca2+ dynamics.

  1. The spikes from Richtmyer-Meshkov instabilities in pused power cylindrical experiments

    Science.gov (United States)

    Rousculp, Chris; Cheng, Baolian; Oro, David; Griego, Jeffrey; Patten, Austin; Neukirch, Levi; Reinovsky, Robert; Turchi, Peter; Bradley, Joeph; Reass, Wlliam; Fierro, Franklin; Saunders, Alexsander; Mariam, Fesseha; Freeman, Matthew; Tang, Zhaowen

    2017-06-01

    The time evolution of the metal spikes resulting from the Richtmyer-Meshkov instability (RMI) of single-mode perturbations on the inside surface of a tin sample in cylindrical geometry has been measured for the first time. The shock condition was produced by a magnetically driven aluminum flyer utilizing the PHELIX capacitor bank. By varying the flyer velocity, a set of experiments conducted at the Los Alamos National Laboratory has explored the RMI evolution in the different release states (fluid, mixed, solid) of tin. The perturbation inversion and growth rate of the spikes were diagnosed in each experiment with a 21-image proton radiography (pRad) movie. Both theoretical model and numerical simulations are performed. Numerical simulations, theory and experimental data are in good agreement. Detailed analysis of the spike growth rates, comparison to planer geometry, as well as theory and computations will be presented. This work was conducted under the auspices of the U.S. Department of Energy by the Los Alamos National Laboratory under Contract No. W-7405-ENG-36.

  2. Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain-machine interfaces

    Science.gov (United States)

    Liao, Yuxi; She, Xiwei; Wang, Yiwen; Zhang, Shaomin; Zhang, Qiaosheng; Zheng, Xiaoxiang; Principe, Jose C.

    2015-12-01

    Objective. Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. Approach. In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat’s motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. Main results. Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. Significance. These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.

  3. Variations in interpulse interval of double action potentials during propagation in single neurons.

    Science.gov (United States)

    Villagran-Vargas, Edgar; Rodríguez-Sosa, Leonardo; Hustert, Reinhold; Blicher, Andreas; Laub, Katrine; Heimburg, Thomas

    2013-02-01

    In this work, we analyzed the interpulse interval (IPI) of doublets and triplets in single neurons of three biological models. Pulse trains with two or three spikes originate from the process of sensory mechanotransduction in neurons of the locust femoral nerve, as well as through spontaneous activity both in the abdominal motor neurons and the caudal photoreceptor of the crayfish. We show that the IPI for successive low-frequency single action potentials, as recorded with two electrodes at two different points along a nerve axon, remains constant. On the other hand, IPI in doublets either remains constant, increases or decreases by up to about 3 ms as the pair propagates. When IPI increases, the succeeding pulse travels at a slower speed than the preceding one. When IPI is reduced, the succeeding pulse travels faster than the preceding one and may exceed the normal value for the specific neuron. In both cases, IPI increase and reduction, the speed of the preceding pulse differs slightly from the normal value, therefore the two pulses travel at different speeds in the same nerve axon. On the basis of our results, we may state that the effect of attraction or repulsion in doublets suggests a tendency of the spikes to reach a stable configuration. We strongly suggest that the change in IPI during spike propagation of doublets opens up a whole new realm of possibilities for neural coding and may have major implications for understanding information processing in nervous systems. Copyright © 2012 Wiley Periodicals, Inc.

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

    Science.gov (United States)

    Sun, Mingming; Qu, Jianhua

    2017-10-01

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

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

    Science.gov (United States)

    Bi, Zedong; Zhou, Changsong

    2016-01-01

    Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP) and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded), by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF) neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy). PMID:27555816

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

    Directory of Open Access Journals (Sweden)

    Zedong Bi

    2016-08-01

    Full Text Available Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded, by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy.

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

    OpenAIRE

    Bi, Zedong; Zhou, Changsong

    2016-01-01

    Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wis...

  8. Stress-Induced Impairment of a Working Memory Task: Role of Spiking Rate and Spiking History Predicted Discharge

    Science.gov (United States)

    Devilbiss, David M.; Jenison, Rick L.; Berridge, Craig W.

    2012-01-01

    Stress, pervasive in society, contributes to over half of all work place accidents a year and over time can contribute to a variety of psychiatric disorders including depression, schizophrenia, and post-traumatic stress disorder. Stress impairs higher cognitive processes, dependent on the prefrontal cortex (PFC) and that involve maintenance and integration of information over extended periods, including working memory and attention. Substantial evidence has demonstrated a relationship between patterns of PFC neuron spiking activity (action-potential discharge) and components of delayed-response tasks used to probe PFC-dependent cognitive function in rats and monkeys. During delay periods of these tasks, persistent spiking activity is posited to be essential for the maintenance of information for working memory and attention. However, the degree to which stress-induced impairment in PFC-dependent cognition involves changes in task-related spiking rates or the ability for PFC neurons to retain information over time remains unknown. In the current study, spiking activity was recorded from the medial PFC of rats performing a delayed-response task of working memory during acute noise stress (93 db). Spike history-predicted discharge (SHPD) for PFC neurons was quantified as a measure of the degree to which ongoing neuronal discharge can be predicted by past spiking activity and reflects the degree to which past information is retained by these neurons over time. We found that PFC neuron discharge is predicted by their past spiking patterns for nearly one second. Acute stress impaired SHPD, selectively during delay intervals of the task, and simultaneously impaired task performance. Despite the reduction in delay-related SHPD, stress increased delay-related spiking rates. These findings suggest that neural codes utilizing SHPD within PFC networks likely reflects an additional important neurophysiological mechanism for maintenance of past information over time. Stress

  9. The effects of aging, physical training, and a single bout of exercise on mitochondrial protein expression in human skeletal muscle.

    Science.gov (United States)

    Bori, Zoltan; Zhao, Zhongfu; Koltai, Erika; Fatouros, Ioannis G; Jamurtas, Athanasios Z; Douroudos, Ioannis I; Terzis, Gerasimos; Chatzinikolaou, Athanasios; Sovatzidis, Apostolos; Draganidis, Dimitrios; Boldogh, Istvan; Radak, Zsolt

    2012-06-01

    Aging results in a significant decline in aerobic capacity and impaired mitochondrial function. We have tested the effects of moderate physical activity on aerobic capacity and a single bout of exercise on the expression profile of mitochondrial biogenesis, and fusion and fission related genes in skeletal muscle of human subjects. Physical activity attenuated the aging-associated decline in VO2 max (pAging increased and a single exercise bout decreased the expression of nuclear respiratory factor-1 (NRF1), while the transcription factor A (TFAM) expression showed a strong relationship with VO(2max) and increased significantly in the young physically active group. Mitochondrial fission representing FIS1 was induced by regular physical activity, while a bout of exercise decreased fusion-associated gene expression. The expression of polynucleotide phosphorylase (PNPase) changed inversely in young and old groups and decreased with aging. The A2 subunit of cyclic AMP-activated protein kinase (AMPK) was induced by a single bout of exercise in skeletal muscle samples of both young and old subjects (pphysical activity increases a larger number of mitochondrial biogenesis-related gene expressions in young individuals than in aged subjects. Mitochondrial fission is impaired by aging and could be one of the most sensitive markers of the age-associated decline in the adaptive response to physical activity. Copyright © 2012 Elsevier Inc. All rights reserved.

  10. Resistance Training with Single vs. Multi-joint Exercises at Equal Total Load Volume: Effects on Body Composition, Cardiorespiratory Fitness, and Muscle Strength

    Science.gov (United States)

    Paoli, Antonio; Gentil, Paulo; Moro, Tatiana; Marcolin, Giuseppe; Bianco, Antonino

    2017-01-01

    The present study aimed to compare the effects of equal-volume resistance training performed with single-joint (SJ) or multi-joint exercises (MJ) on VO2max, muscle strength and body composition in physically active males. Thirty-six participants were divided in two groups: SJ group (n = 18, 182.1 ± 5.2, 80.03 ± 2.78 kg, 23.5 ± 2.7 years) exercised with only SJ exercises (e.g., dumbbell fly, knee extension, etc.) and MJ group (n = 18, 185.3 ± 3.6 cm, 80.69 ± 2.98 kg, 25.5 ± 3.8 years) with only MJ exercises (e.g., bench press, squat, etc.). The total work volume (repetitions × sets × load) was equated between groups. Training was performed three times a week for 8 weeks. Before and after the training period, participants were tested for VO2max, body composition, 1 RM on the bench press, knee extension and squat. Analysis of covariance (ANCOVA) was used to compare post training values between groups, using baseline values as covariates. According to the results, both groups decreased body fat and increased fat free mass with no difference between them. Whilst both groups significantly increased cardiorespiratory fitness and maximal strength, the improvements in MJ group were higher than for SJ in VO2max (5.1 and 12.5% for SJ and MJ), bench press 1 RM (8.1 and 10.9% for SJ and MJ), knee extension 1 RM (12.4 and 18.9% for SJ and MJ) and squat 1 RM (8.3 and 13.8% for SJ and MJ). In conclusion, when total work volume was equated, RT programs involving MJ exercises appear to be more efficient for improving muscle strength and maximal oxygen consumption than programs involving SJ exercises, but no differences were found for body composition. PMID:29312007

  11. Does arousal interfere with operant conditioning of spike-wave discharges in genetic epileptic rats?

    Science.gov (United States)

    Osterhagen, Lasse; Breteler, Marinus; van Luijtelaar, Gilles

    2010-06-01

    One of the ways in which brain computer interfaces can be used is neurofeedback (NF). Subjects use their brain activation to control an external device, and with this technique it is also possible to learn to control aspects of the brain activity by operant conditioning. Beneficial effects of NF training on seizure occurrence have been described in epileptic patients. Little research has been done about differentiating NF effectiveness by type of epilepsy, particularly, whether idiopathic generalized seizures are susceptible to NF. In this experiment, seizures that manifest themselves as spike-wave discharges (SWDs) in the EEG were reinforced during 10 sessions in 6 rats of the WAG/Rij strain, an animal model for absence epilepsy. EEG's were recorded before and after the training sessions. Reinforcing SWDs let to decreased SWD occurrences during training; however, the changes during training were not persistent in the post-training sessions. Because behavioural states are known to have an influence on the occurrence of SWDs, it is proposed that the reinforcement situation increased arousal which resulted in fewer SWDs. Additional tests supported this hypothesis. The outcomes have implications for the possibility to train SWDs with operant learning techniques. Copyright (c) 2010 Elsevier B.V. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Cheng eLy

    2012-03-01

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

  13. SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings.

    Science.gov (United States)

    Pastore, Vito Paolo; Godjoski, Aleksandar; Martinoia, Sergio; Massobrio, Paolo

    2018-01-01

    We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

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

    Science.gov (United States)

    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.

  15. From spiking neurons to brain waves

    NARCIS (Netherlands)

    Visser, S.

    2013-01-01

    No single model would be able to capture all processes in the brain at once, since its interactions are too numerous and too complex. Therefore, it is common practice to simplify the parts of the system. Typically, the goal is to describe the collective action of many underlying processes, without

  16. Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity.

    Science.gov (United States)

    Osogami, Takayuki; Otsuka, Makoto

    2015-09-16

    An artificial neural network, such as a Boltzmann machine, can be trained with the Hebb rule so that it stores static patterns and retrieves a particular pattern when an associated cue is presented to it. Such a network, however, cannot effectively deal with dynamic patterns in the manner of living creatures. Here, we design a dynamic Boltzmann machine (DyBM) and a learning rule that has some of the properties of spike-timing dependent plasticity (STDP), which has been postulated for biological neural networks. We train a DyBM consisting of only seven neurons in a way that it memorizes the sequence of the bitmap patterns in an alphabetical image "SCIENCE" and its reverse sequence and retrieves either sequence when a partial sequence is presented as a cue. The DyBM is to STDP as the Boltzmann machine is to the Hebb rule.

  17. FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

    Directory of Open Access Journals (Sweden)

    T. Pasupathi

    2014-05-01

    Full Text Available Image recognition is a technology which can be used in various applications such as medical image recognition systems, security, defense video tracking, and factory automation. In this paper we present a novel pipelined architecture of an adaptive integrated Artificial Neural Network for image recognition. In our proposed work we have combined the feature of spiking neuron concept with ANN to achieve the efficient architecture for image recognition. The set of training images are trained by ANN and target output has been identified. Real time videos are captured and then converted into frames for testing purpose and the image were recognized. The machine can operate at up to 40 frames/sec using images acquired from the camera. The system has been implemented on XC3S400 SPARTAN-3 Field Programmable Gate Arrays.

  18. Alcohol ingestion impairs maximal post-exercise rates of myofibrillar protein synthesis following a single bout of concurrent training.

    Directory of Open Access Journals (Sweden)

    Evelyn B Parr

    Full Text Available INTRODUCTION: The culture in many team sports involves consumption of large amounts of alcohol after training/competition. The effect of such a practice on recovery processes underlying protein turnover in human skeletal muscle are unknown. We determined the effect of alcohol intake on rates of myofibrillar protein synthesis (MPS following strenuous exercise with carbohydrate (CHO or protein ingestion. METHODS: In a randomized cross-over design, 8 physically active males completed three experimental trials comprising resistance exercise (8×5 reps leg extension, 80% 1 repetition maximum followed by continuous (30 min, 63% peak power output (PPO and high intensity interval (10×30 s, 110% PPO cycling. Immediately, and 4 h post-exercise, subjects consumed either 500 mL of whey protein (25 g; PRO, alcohol (1.5 g·kg body mass⁻¹, 12±2 standard drinks co-ingested with protein (ALC-PRO, or an energy-matched quantity of carbohydrate also with alcohol (25 g maltodextrin; ALC-CHO. Subjects also consumed a CHO meal (1.5 g CHO·kg body mass⁻¹ 2 h post-exercise. Muscle biopsies were taken at rest, 2 and 8 h post-exercise. RESULTS: Blood alcohol concentration was elevated above baseline with ALC-CHO and ALC-PRO throughout recovery (P<0.05. Phosphorylation of mTOR(Ser2448 2 h after exercise was higher with PRO compared to ALC-PRO and ALC-CHO (P<0.05, while p70S6K phosphorylation was higher 2 h post-exercise with ALC-PRO and PRO compared to ALC-CHO (P<0.05. Rates of MPS increased above rest for all conditions (∼29-109%, P<0.05. However, compared to PRO, there was a hierarchical reduction in MPS with ALC-PRO (24%, P<0.05 and with ALC-CHO (37%, P<0.05. CONCLUSION: We provide novel data demonstrating that alcohol consumption reduces rates of MPS following a bout of concurrent exercise, even when co-ingested with protein. We conclude that alcohol ingestion suppresses the anabolic response in skeletal muscle and may therefore impair recovery and adaptation

  19. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

    Science.gov (United States)

    Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram

    2017-04-01

    Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.

  20. Intensive virtual reality-based training for upper limb motor function in chronic stroke: a feasibility study using a single case experimental design and fMRI.

    Science.gov (United States)

    Schuster-Amft, Corina; Henneke, Andrea; Hartog-Keisker, Birgit; Holper, Lisa; Siekierka, Ewa; Chevrier, Edith; Pyk, Pawel; Kollias, Spyros; Kiper, Daniel; Eng, Kynan

    2015-01-01

    To evaluate feasibility and neurophysiological changes after virtual reality (VR)-based training of upper limb (UL) movements. Single-case A-B-A-design with two male stroke patients (P1:67 y and 50 y, 3.5 and 3 y after onset) with UL motor impairments, 45-min therapy sessions 5×/week over 4 weeks. Patients facing screen, used bimanual data gloves to control virtual arms. Three applications trained bimanual reaching, grasping, hand opening. Assessments during 2-week baseline, weekly during intervention, at 3-month follow-up (FU): Goal Attainment Scale (GAS), Chedoke Arm and Hand Activity Inventory (CAHAI), Chedoke-McMaster Stroke Assessment (CMSA), Extended Barthel Index (EBI), Motor Activity Log (MAL). Functional magnetic resonance imaging scans (FMRI) before, immediately after treatment and at FU. P1 executed 5478 grasps (paretic arm). Improvements in CAHAI (+4) were maintained at FU. GAS changed to +1 post-test and +2 at FU. P2 executed 9835 grasps (paretic arm). CAHAI improvements (+13) were maintained at FU. GAS scores changed to -1 post-test and +1 at FU. MAL scores changed from 3.7 at pre-test to 5.5 post-test and 3.3 at FU. The VR-based intervention was feasible, safe, and intense. Adjustable application settings maintained training challenge and patient motivation. ADL-relevant UL functional improvements persisted at FU and were related to changed cortical activation patterns. Implications for Rehabilitation YouGrabber trains uni- and bimanual upper motor function. Its application is feasible, safe, and intense. The control of the virtual arms can be done in three main ways: (a) normal (b) virtual mirror therapy, or (c) virtual following. The mirroring feature provides an illusion of affected limb movements during the period when the affected upper limb (UL) is resting. The YouGrabber training led to ADL-relevant UL functional improvements that were still assessable 12 weeks after intervention finalization and were related to changed cortical

  1. Spike morphology in blast-wave-driven instability experiments

    International Nuclear Information System (INIS)

    Kuranz, C. C.; Drake, R. P.; Grosskopf, M. J.; Fryxell, B.; Budde, A.; Hansen, J. F.; Miles, A. R.; Plewa, T.; Hearn, N.; Knauer, J.

    2010-01-01

    The laboratory experiments described in the present paper observe the blast-wave-driven Rayleigh-Taylor instability with three-dimensional (3D) initial conditions. About 5 kJ of energy from the Omega laser creates conditions similar to those of the He-H interface during the explosion phase of a supernova. The experimental target is a 150 μm thick plastic disk followed by a low-density foam. The plastic piece has an embedded, 3D perturbation. The basic structure of the pattern is two orthogonal sine waves where each sine wave has an amplitude of 2.5 μm and a wavelength of 71 μm. In some experiments, an additional wavelength is added to explore the interaction of modes. In experiments with 3D initial conditions the spike morphology differs from what has been observed in other Rayleigh-Taylor experiments and simulations. Under certain conditions, experimental radiographs show some mass extending from the interface to the shock front. Current simulations show neither the spike morphology nor the spike penetration observed in the experiments. The amount of mass reaching the shock front is analyzed and potential causes for the spike morphology and the spikes reaching the shock are discussed. One such hypothesis is that these phenomena may be caused by magnetic pressure, generated by an azimuthal magnetic field produced by the plasma dynamics.

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

    Science.gov (United States)

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

    2013-09-01

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

  3. Multiplexed Spike Coding and Adaptation in the Thalamus

    Directory of Open Access Journals (Sweden)

    Rebecca A. Mease

    2017-05-01

    Full Text Available High-frequency “burst” clusters of spikes are a generic output pattern of many neurons. While bursting is a ubiquitous computational feature of different nervous systems across animal species, the encoding of synaptic inputs by bursts is not well understood. We find that bursting neurons in the rodent thalamus employ “multiplexing” to differentially encode low- and high-frequency stimulus features associated with either T-type calcium “low-threshold” or fast sodium spiking events, respectively, and these events adapt differently. Thus, thalamic bursts encode disparate information in three channels: (1 burst size, (2 burst onset time, and (3 precise spike timing within bursts. Strikingly, this latter “intraburst” encoding channel shows millisecond-level feature selectivity and adapts across statistical contexts to maintain stable information encoded per spike. Consequently, calcium events both encode low-frequency stimuli and, in parallel, gate a transient window for high-frequency, adaptive stimulus encoding by sodium spike timing, allowing bursts to efficiently convey fine-scale temporal information.

  4. Relationship of Physical Function to Single Muscle Fiber Contractility in Older Adults: Effects of Resistance Training with and without Caloric Restriction.

    Science.gov (United States)

    Wang, Zhong-Min; Leng, Xiaoyan; Messi, María Laura; Choi, Seung J; Marsh, Anthony P; Nicklas, Barbara; Delbono, Osvaldo

    2018-03-13

    Previous studies support beneficial effects of both resistance exercise training (RT) and caloric restriction (CR) on skeletal muscle strength and physical performance. The goal of this study was to determine the effects of adding CR to RT on single-muscle fiber contractility responses to RT in older overweight and obese adults. We analyzed contractile properties in 1,253 single myofiber from muscle biopsies of the vastus lateralis, as well as physical performance and thigh muscle volume, in 31 older (65-80 yrs), overweight or obese (body mass index= 27-35 kg/m2) men (n=19) and women (n=12) who were randomly assigned to a standardized, progressive RT intervention with CR (RT+CR; n=15) or without CR (RT; n=16) for 5 months. Both interventions evoked an increase in force normalized to CSA, in type-I and type-II fibers and knee extensor quality. However, these improvements were not different between intervention groups. In the RT group, changes in total thigh fat volume inversely correlated with changes in type-II fiber force (r = -0.691; p=0.019). Within the RT+CR group, changes in gait speed correlated positively with changes in type-I fiber CSA (r=0.561; p=0.030). In addition, increases in type-I normalized fiber force were related to decreases in thigh intermuscular fat volume (r= -0.539; p= 0.038). Single muscle fiber force and knee extensor quality improve with RT and RT+CR; however, CR does not enhance improvements in single muscle fiber contractility or whole muscle in response to RT in older overweight and obese men and women.

  5. A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine

    Directory of Open Access Journals (Sweden)

    Basabdatta Sen-Bhattacharya

    2017-08-01

    Full Text Available We present a spiking neural network model of the thalamic Lateral Geniculate Nucleus (LGN developed on SpiNNaker, which is a state-of-the-art digital neuromorphic hardware built with very-low-power ARM processors. The parallel, event-based data processing in SpiNNaker makes it viable for building massively parallel neuro-computational frameworks. The LGN model has 140 neurons representing a “basic building block” for larger modular architectures. The motivation of this work is to simulate biologically plausible LGN dynamics on SpiNNaker. Synaptic layout of the model is consistent with biology. The model response is validated with existing literature reporting entrainment in steady state visually evoked potentials (SSVEP—brain oscillations corresponding to periodic visual stimuli recorded via electroencephalography (EEG. Periodic stimulus to the model is provided by: a synthetic spike-train with inter-spike-intervals in the range 10–50 Hz at a resolution of 1 Hz; and spike-train output from a state-of-the-art electronic retina subjected to a light emitting diode flashing at 10, 20, and 40 Hz, simulating real-world visual stimulus to the model. The resolution of simulation is 0.1 ms to ensure solution accuracy for the underlying differential equations defining Izhikevichs neuron model. Under this constraint, 1 s of model simulation time is executed in 10 s real time on SpiNNaker; this is because simulations on SpiNNaker work in real time for time-steps dt ⩾ 1 ms. The model output shows entrainment with both sets of input and contains harmonic components of the fundamental frequency. However, suppressing the feed-forward inhibition in the circuit produces subharmonics within the gamma band (>30 Hz implying a reduced information transmission fidelity. These model predictions agree with recent lumped-parameter computational model-based predictions, using conventional computers. Scalability of the framework is demonstrated by a multi

  6. Effect of a single-session meditation training to reduce stress and improve quality of life among health care professionals: a "dose-ranging" feasibility study.

    Science.gov (United States)

    Prasad, Kavita; Wahner-Roedler, Dietlind L; Cha, Stephen S; Sood, Amit

    2011-01-01

    The primary aim of the study was to assess the feasibility of incorporating a single-session meditation-training program into the daily activities of healthy employees of a tertiary-care academic medical center. The study also assessed the most preferred duration of meditation and the effect of the meditation program on perceived stress, anxiety, and overall quality of life (QOL). Seventeen healthy clinic employees were recruited for this study. After an initial group instruction session covering basic information about meditation, Paced Breathing Meditation (PBM) was taught to the participants. Participants were instructed to self-practice meditation with the help of a DVD daily for a total of 4 weeks. The DVD had three different programs of 5, 15, and 30 minutes with a menu option to choose one of the programs. (1) Patient diary, (2) Perceived Stress Scale (PSS), (3) Linear Analogue Self-Assessment (LASA), (4) Smith Anxiety Scale (SAS). Primary outcome measures were compared using the paired t-test. All participants were female; median age was 48 years (range 33-60 y). The 5-minute meditation session was practiced by 14 participants a total of 137 times during the 4-week trial period, the 15-minute session by 16 participants a total of 223 times, and the 30-minute session by 13 participants 71 times. The median number of days practiced was 25 (range 10-28 d); the average total time practiced was 394 minutes (range 55-850 min). After 4 weeks of practice, the scores of the following instruments improved significantly from baseline: PSS (P meditation in a single training session to health care employees. The study shows that 15 minutes once or twice a day is the most feasible duration of meditation practice. The study also provides promising preliminary efficacy data of this program for improving stress, anxiety, and QOL.

  7. Acute Effects of Different Stretching Techniques on the Number of Repetitions in A Single Lower Body Resistance Training Session

    Directory of Open Access Journals (Sweden)

    Sá Marcos A.

    2015-03-01

    Full Text Available This study aimed to investigate the acute effects of passive static and ballistic stretching on maximal repetition performance during a resistance training session (RTS. Nine male subjects underwent three experimental conditions: ballistic stretching (BS; passive static stretching (PSS; and a specific warm-up (SW. The RTS was composed of three sets of 12RM for the following exercises: leg press 45 (LP, leg extension (LE, leg curl (LC, and plantar flexors (PF. Performance of six sessions was assessed 48 hours apart. The first visit consisted of a familiarization session including stretching methods and exercises used in the RTS. On the second and third visit, a strength test and retest were performed. During the fourth to the sixth visit, the volunteers randomly performed the following protocols: BS+RTS; PSS+RTS; or SW+RTS. For the sum of the RM number of each three-set exercise, significant differences were found between PSS vs. SW for the LP (p = 0.001; LE (p = 0.005; MF (p = 0.001; and PF (p = 0.038. For the comparison between the methods of stretching PSS vs. BS, significant differences were found only for the FP (p = 0.019. When analyzing the method of stretching BS vs. SW, significant differences were found for the LP (p = 0.014 and MF (p = 0.002. For the total sum of the RM number of three sets of the four exercises that composed the RTS, significant differences were observed (p < 0.05 in the following comparisons: PPS vs. SW (p = 0.001, PPS vs. BS (p = 0.008, and BS vs. SW (p = 0.002. Accordingly, the methods of passive static and ballistic stretching should not be recommended before a RTS.

  8. Effectiveness of ACT-Based Parenting Training to Mothers on the Depression of Children with Cleft Lip and Palate: A Single Subject Study

    Directory of Open Access Journals (Sweden)

    محمد صالح فقیهی

    2017-06-01

    Full Text Available The purpose of this study was to determine the effectiveness of Parenting Training based on Acceptance and Commitment Therapy (ACT to mothers on the depression of children with cleft lip and palate. The research method was based on a single case and individual intervention study. The sample was constituted of 65 Isfahanian children with cleft lip and palate. Parenting skills based on ACT were taught to five mothers of children with cleft lip and palate who achieved the minimum score in screening. After three baseline sessions for each child, ACT parenting skills were taught to their mothers in 8 individual sessions companied with testing the child’s depression in every session. Three follow-up sessions after 15 days, 1 month and 3 months were set to evaluate children’s depression. The Kovacs Children’s Depression Inventory (CDI was used to test the children’s depression. The results were analyzed with visual analysis and descriptive statistics. This particular intervention was effective on depression. Based on the results of the present study, it can be concluded that ACT parenting training to mothers of children with cleft lips and palates was effective on reducing depression and that an on-time intervention can improve these children's depression.

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

    Directory of Open Access Journals (Sweden)

    Rainer Engelken

    2016-08-01

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

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

    DEFF Research Database (Denmark)

    Jensen, Hans Grinsted; Anderson, Kym

    2017-01-01

    When prices spike in international grain markets, national governments often reduce the extent to which that spike affects their domestic food markets. Those actions exacerbate the price spike and international welfare transfer associated with that terms of trade change. Several recent analyses...... have assessed the extent to which those policies contributed to the 2006–08 international price rises but only by focusing on one commodity or by using a back-of-the envelope (BOTE) method. The present more comprehensive analysis uses a global, economy-wide model that is able to take account...... of the interactions between markets for farm products that are closely related in production and/or consumption and able to estimate the impacts of those insulating policies on grain prices and on the grain trade and economic welfare of the world's various countries. Our results support the conclusion from earlier...

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

    DEFF Research Database (Denmark)

    Jensen, Hans Grinsted; Anderson, Kym

    When prices spike in international grain markets, national governments often reduce the extent to which that spike affects their domestic food markets. Those actions exacerbate the price spike and international welfare transfer associated with that terms of trade change. Several recent analyses...... have assessed the extent to which those policies contributed to the 2006-08 international price rise, but only by focusing on one commodity or using a back-of-the envelope (BOTE) method. This paper provides a more-comprehensive analysis using a global economy-wide model that is able to take account...... of the interactions between markets for farm products that are closely related in production and/or consumption, and able to estimate the impacts of those insulating policies on grain prices and on the grain trade and economic welfare of the world’s various countries. Our results support the conclusion from earlier...

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

    DEFF Research Database (Denmark)

    Proietti, Tomasso; Haldrup, Niels; Knapik, Oskar

    from the normal price, where the latter is defined as the expectation arising from a model accounting for long memory at the zero and at the weekly seasonal frequencies, given the knowledge of the past realizations. Hence, a spike is associated to a time series innovation with size larger than......Electricity spot prices are subject to transitory sharp movements commonly referred to as spikes. The paper aims at assessing their effects on model based inferences and predictions, with reference to the Nord Pool power exchange. We identify a spike as a price value which deviates substantially...... a specified threshold. The latter regulates the robustness of the estimates of the underlying price level and it is chosen by a data driven procedure that focuses on the ability to predict future prices. The normal price is computed by a modified Kalman filter, which robustifies the inferences by cleaning...

  13. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan

    2015-04-01

    Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

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

    International Nuclear Information System (INIS)

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

    1998-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Carlo Lucheroni

    2012-01-01

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

  16. Lion (Panthera leo) and caracal (Caracal caracal) type IIx single muscle fibre force and power exceed that of trained humans.

    Science.gov (United States)

    Kohn, Tertius A; Noakes, Timothy D

    2013-03-15

    This study investigated for the first time maximum force production, shortening velocity (Vmax) and power output in permeabilised single muscle fibres at 12°C from lion, Panthera leo (Linnaeus 1758), and caracal, Caracal caracal (Schreber 1776), and compared the values with those from human cyclists. Additionally, the use and validation of previously frozen tissue for contractile experiments is reported. Only type IIx muscle fibres were identified in the caracal sample, whereas type IIx and only two type I fibres were found in the lion sample. Only pure type I and IIa, and hybrid type IIax fibres were identified in the human samples - there were no pure type IIx fibres. Nevertheless, compared with all the human fibre types, the lion and caracal fibres were smaller (Plion: 3008±151 μm(2), caracal: 2583±221 μm(2)). On average, the felid type IIx fibres produced significantly greater force (191-211 kN m(-2)) and ~3 times more power (29.0-30.3 kN m(-2) fibre lengths s(-1)) than the human IIax fibres (100-150 kN m(-2), 4-11 kN m(-2) fibre lengths s(-1)). Vmax values of the lion type IIx fibres were also higher than those of human type IIax fibres. The findings suggest that the same fibre type may differ substantially between species and potential explanations are discussed.

  17. Single-session emotion regulation skills training to reduce aggression in combat veterans: A clinical innovation case study.

    Science.gov (United States)

    Miles, Shannon R; Thompson, Karin E; Stanley, Melinda A; Kent, Thomas A

    2016-05-01

    Posttraumatic stress disorder (PTSD) is common among returning veterans, and aggression frequently co-occurs with PTSD. Veterans with PTSD most commonly engage in impulsive aggression, or aggression that is emotionally charged, unplanned, and uncontrolled, rather than premeditated aggression, which is planned and controlled. Previous research demonstrated a variety of emotions can result in aggression, rather than the traditional conceptualization that only anger leads to aggression. In a veteran sample, deficiencies in the ability to regulate emotions (emotion dysregulation) mediated the relationship between PTSD and impulsive aggression. These results suggest that teaching veterans with PTSD and impulsive aggression how to regulate emotions may decrease aggression. The cases presented illustrate the use of an innovative, single-session emotion regulation treatment for combat veterans with PTSD. Two cases are presented to generate hypotheses on who might benefit from this treatment in the future. The two male veterans treated with this protocol differed in how frequently they used the emotion regulation skills after the treatment and in their treatment outcomes. Teaching veterans how to regulate their emotions in a condensed time frame may be beneficial for certain veterans, and further research on this brief treatment is warranted. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  18. A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

    Science.gov (United States)

    Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

    2017-05-01

    This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

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

    Science.gov (United States)

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

    2010-09-01

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

  20. A new approach to detect the coding rule of the cortical spiking model in the information transmission.

    Science.gov (United States)

    Nazari, Soheila; Faez, Karim; Janahmadi, Mahyar

    2018-03-01

    Investigation of the role of the local field potential (LFP) fluctuations in encoding the received sensory information by the nervous system remains largely unknown. On the other hand, transmission of these translation rules in information transmission between the structure of sensory stimuli and the cortical oscillations to the bio-inspired artificial neural networks operating at the efficiency of the nervous system is still a vague puzzle. In order to move towards this important goal, computational neuroscience tools can be useful so, we simulated a large-scale network of excitatory and inhibitory spiking neurons with synaptic connections consisting of AMPA and GABA currents as a model of cortical populations. Spiking network was equipped with spike-based unsupervised weight optimization based on the dynamical behavior of the excitatory (AMPA) and inhibitory (GABA) synapses using Spike Timing Dependent Plasticity (STDP) on the MNIST benchmark and we specified how the generated LFP by the network contained information about input patterns. The main result of this article is that the calculated coefficients of Prolate spheroidal wave functions (PSWF) from the input pattern with mean square error (MSE) criterion and power spectrum of LFP with maximum correntropy criterion (MCC) are equal. The more important result is that 82.3% of PSWF coefficients are the same as the connecting weights of the cortical neurons to the classifying neurons after the completion of the training process. Higher compliance percentage of coefficients with synaptic weights (82.3%) gives the expectance us that this coding rule will be able to extend to biological systems. Eventually, we introduced the cortical spiking network as an information channel, which transmits the information of the input pattern in the form of PSWF coefficients to the power spectrum of the output generated LFP. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Stochastic hybrid model of spontaneous dendritic NMDA spikes

    International Nuclear Information System (INIS)

    Bressloff, Paul C; Newby, Jay M

    2014-01-01

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

  2. Bayesian Inference for Structured Spike and Slab Priors

    DEFF Research Database (Denmark)

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

    2014-01-01

    Sparse signal recovery addresses the problem of solving underdetermined linear inverse problems subject to a sparsity constraint. We propose a novel prior formulation, the structured spike and slab prior, which allows to incorporate a priori knowledge of the sparsity pattern by imposing a spatial...... Gaussian process on the spike and slab probabilities. Thus, prior information on the structure of the sparsity pattern can be encoded using generic covariance functions. Furthermore, we provide a Bayesian inference scheme for the proposed model based on the expectation propagation framework. Using...

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

    International Nuclear Information System (INIS)

    Chang Wonil; Jin, Dezhe Z.

    2009-01-01

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

  4. A Spiking Neural Network in sEMG Feature Extraction.

    Science.gov (United States)

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

    2015-11-03

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

  5. Method for spiking soil samples with organic compounds

    DEFF Research Database (Denmark)

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

    2002-01-01

    We examined the harmful side effects on indigenous soil microorganisms of two organic solvents, acetone and dichloromethane, that are normally used for spiking of soil with polycyclic aromatic hydrocarbons for experimental purposes. The solvents were applied in two contamination protocols to either...... higher than in control soil, probably due mainly to release of predation from indigenous protozoa. In order to minimize solvent effects on indigenous soil microorganisms when spiking native soil samples with compounds having a low water solubility, we propose a common protocol in which the contaminant...

  6. The Role of Artificial Intelligence in Diagnostic Radiology: A Survey at a Single Radiology Residency Training Program.

    Science.gov (United States)

    Collado-Mesa, Fernando; Alvarez, Edilberto; Arheart, Kris

    2018-02-21

    Advances in artificial intelligence applied to diagnostic radiology are predicted to have a major impact on this medical specialty. With the goal of establishing a baseline upon which to build educational activities on this topic, a survey was conducted among trainees and attending radiologists at a single residency program. An anonymous questionnaire was distributed. Comparisons of categorical data between groups (trainees and attending radiologists) were made using Pearson χ 2 analysis or an exact analysis when required. Comparisons were made using the Wilcoxon rank sum test when the data were not normally distributed. An α level of 0.05 was used. The overall response rate was 66% (69 of 104). Thirty-six percent of participants (n = 25) reported not having read a scientific medical article on the topic of artificial intelligence during the past 12 months. Twenty-nine percent of respondents (n = 12) reported using artificial intelligence tools during their daily work. Trainees were more likely to express doubts on whether they would have pursued diagnostic radiology as a career had they known of the potential impact artificial intelligence is predicted to have on the specialty (P = .0254) and were also more likely to plan to learn about the topic (P = .0401). Radiologists lack exposure to current scientific medical articles on artificial intelligence. Trainees are concerned by the implications artificial intelligence may have on their jobs and desire to learn about the topic. There is a need to develop educational resources to help radiologists assume an active role in guiding and facilitating the development and implementation of artificial intelligence tools in diagnostic radiology. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  7. Advanced Cardiac Resuscitation Evaluation (ACRE: A randomised single-blind controlled trial of peer-led vs. expert-led advanced resuscitation training

    Directory of Open Access Journals (Sweden)

    Hughes Thomas C

    2010-01-01

    Full Text Available Abstract Background Advanced resuscitation skills training is an important and enjoyable part of medical training, but requires small group instruction to ensure active participation of all students. Increases in student numbers have made this increasingly difficult to achieve. Methods A single-blind randomised controlled trial of peer-led vs. expert-led resuscitation training was performed using a group of sixth-year medical students as peer instructors. The expert instructors were a senior and a middle grade doctor, and a nurse who is an Advanced Life Support (ALS Instructor. A power calculation showed that the trial would have a greater than 90% chance of rejecting the null hypothesis (that expert-led groups performed 20% better than peer-led groups if that were the true situation. Secondary outcome measures were the proportion of High Pass grades in each groups and safety incidents. The peer instructors designed and delivered their own course material. To ensure safety, the peer-led groups used modified defibrillators that could deliver only low-energy shocks. Blinded assessment was conducted using an Objective Structured Clinical Examination (OSCE. The checklist items were based on International Liaison Committee on Resuscitation (ILCOR guidelines using Ebel standard-setting methods that emphasised patient and staff safety and clinical effectiveness. The results were analysed using Exact methods, chi-squared and t-test. Results A total of 132 students were randomised: 58 into the expert-led group, 74 into the peer-led group. 57/58 (98% of students from the expert-led group achieved a Pass compared to 72/74 (97% from the peer-led group: Exact statistics confirmed that it was very unlikely (p = 0.0001 that the expert-led group was 20% better than the peer-led group. There were no safety incidents, and High Pass grades were achieved by 64 (49% of students: 33/58 (57% from the expert-led group, 31/74 (42% from the peer-led group. Exact

  8. Resistance Training with Single vs. Multi-joint Exercises at Equal Total Load Volume: Effects on Body Composition, Cardiorespiratory Fitness, and Muscle Strength

    Directory of Open Access Journals (Sweden)

    Antonio Paoli

    2017-12-01

    Full Text Available The present study aimed to compare the effects of equal-volume resistance training performed with single-joint (SJ or multi-joint exercises (MJ on VO2max, muscle strength and body composition in physically active males. Thirty-six participants were divided in two groups: SJ group (n = 18, 182.1 ± 5.2, 80.03 ± 2.78 kg, 23.5 ± 2.7 years exercised with only SJ exercises (e.g., dumbbell fly, knee extension, etc. and MJ group (n = 18, 185.3 ± 3.6 cm, 80.69 ± 2.98 kg, 25.5 ± 3.8 years with only MJ exercises (e.g., bench press, squat, etc.. The total work volume (repetitions × sets × load was equated between groups. Training was performed three times a week for 8 weeks. Before and after the training period, participants were tested for VO2max, body composition, 1 RM on the bench press, knee extension and squat. Analysis of covariance (ANCOVA was used to compare post training values between groups, using baseline values as covariates. According to the results, both groups decreased body fat and increased fat free mass with no difference between them. Whilst both groups significantly increased cardiorespiratory fitness and maximal strength, the improvements in MJ group were higher than for SJ in VO2max (5.1 and 12.5% for SJ and MJ, bench press 1 RM (8.1 and 10.9% for SJ and MJ, knee extension 1 RM (12.4 and 18.9% for SJ and MJ and squat 1 RM (8.3 and 13.8% for SJ and MJ. In conclusion, when total work volume was equated, RT programs involving MJ exercises appear to be more efficient for improving muscle strength and maximal oxygen consumption than programs involving SJ exercises, but no differences were found for body composition.

  9. Does a single session of high-intensity interval training provoke a transient elevated risk of falling in seniors and adults?

    Science.gov (United States)

    Donath, Lars; Kurz, Eduard; Roth, Ralf; Hanssen, Henner; Schmidt-Trucksäss, Arno; Zahner, Lukas; Faude, Oliver

    2015-01-01

    Balance and strength training can reduce seniors' fall risk up to 50%. Available evidence suggests that acute bouts of neuromuscular and endurance exercise deteriorate postural control. High-intensity endurance training has been successfully applied in different populations. Thus, it seemed valuable to examine the acute effects of high-intensity interval training (HIIT) on neuromuscular performance in seniors and young adults. The acute impact of a HIIT session on balance performance and muscle activity after exercise cessation and during post-exercise recovery was examined in young and old adults. We intended to investigate whether a transient exercise-induced fall-risk may occur in both groups. 20 healthy seniors (age 70 (SD 4) years) and young adults (age 27 (SD 3) years) were examined on 3 days. After exhaustive ramp-like treadmill testing in order to determine maximal heart rate (HRmax) on the first day, either a 4 × 4 min HIIT at 90% of HRmax or a control condition (CON) was randomly performed on the second and third day, respectively. Balance performance (postural sway) was assessed during single limb stance with open eyes (SLEO) and double limb stance with closed eyes (DLEC). EMG was recorded for the soleus (SOL), anterior tibialis (TIB), gastrocnemius (GM) and peroneus longus (PL) muscles at the dominant leg. All measures were collected before, immediately as well as 10, 30 and 45 min after HIIT and CON, respectively. Compared to CON, HIIT induced significant increases of postural sway immediately after exercise cessation during SLEO in both groups (adults: p HIIT (post: p = 0.003, Δ = +14% sway, 10 min post: p = 0.004, Δ = +18% sway). Muscle activity was increased during SLEO for TIB until 10 min post in seniors (0.008 HIIT in adults (p HIIT training may cause an acute 'open-fall-window' with a transient impairment of balance performance for at least 10 min after exercise cessation in both groups. Occluded vision in seniors seems to prolong this period

  10. Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity

    Directory of Open Access Journals (Sweden)

    Peter U. Diehl

    2015-08-01

    Full Text Available In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns, since most of such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e. conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.

  11. Toxicity of nickel-spiked freshwater sediments to benthic invertebrates-Spiking methodology, species sensitivity, and nickel bioavailability

    Science.gov (United States)

    Besser, John M.; Brumbaugh, William G.; Kemble, Nile E.; Ivey, Chris D.; Kunz, James L.; Ingersoll, Christopher G.; Rudel, David

    2011-01-01

    This report summarizes data from studies of the toxicity and bioavailability of nickel in nickel-spiked freshwater sediments. The goal of these studies was to generate toxicity and chemistry data to support development of broadly applicable sediment quality guidelines for nickel. The studies were conducted as three tasks, which are presented here as three chapters: Task 1, Development of methods for preparation and toxicity testing of nickel-spiked freshwater sediments; Task 2, Sensitivity of benthic invertebrates to toxicity of nickel-spiked freshwater sediments; and Task 3, Effect of sediment characteristics on nickel bioavailability. Appendices with additional methodological details and raw chemistry and toxicity data for the three tasks are available online at http://pubs.usgs.gov/sir/2011/5225/downloads/.

  12. A defined network of fast-spiking interneurons in orbitofrontal cortex: responses to behavioral contingencies and ketamine administration

    Directory of Open Access Journals (Sweden)

    Michael C Quirk

    2009-11-01

    Full Text Available Orbitofrontal cortex (OFC is a region of prefrontal cortex implicated in the motivational control of behavior and in related abnormalities seen in psychosis and depression. It has been hypothesized that a critical mechanism in these disorders is the dysfunction of GABAergic interneurons that normally regulate prefrontal information processing. Here, we studied a subclass of interneurons isolated in rat OFC using extracellular waveform and spike train analysis. During performance of a goal-directed behavioral task, the firing of this class of putative fast-spiking (FS interneurons showed robust temporal correlations indicative of a functionally coherent network. FS cell activity also co-varied with behavioral response latency, a key indicator of motivational state. Systemic administration of ketamine, a drug that can mimic psychosis, preferentially inhibited this cell class. Together, these results support the idea that OFC-FS interneurons form a critical link in the regulation of motivation by prefrontal circuits during normal and abnormal brain and behavioral states.

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

    Science.gov (United States)

    Panda, Priyadarshini; Roy, Kaushik

    2017-01-01

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

  14. Comparison of a spiking neural network and an MLP for robust identification of generator dynamics in a multimachine power system.

    Science.gov (United States)

    Johnson, Cameron; Venayagamoorthy, Ganesh Kumar; Mitra, Pinaki

    2009-01-01

    The application of a spiking neural network (SNN) and a multi-layer perceptron (MLP) for online identification of generator dynamics in a multimachine power system are compared in this paper. An integrate-and-fire model of an SNN which communicates information via the inter-spike interval is applied. The neural network identifiers are used to predict the speed and terminal voltage deviations one time-step ahead of generators in a multimachine power system. The SNN is developed in two steps: (i) neuron centers determined by offline k-means clustering and (ii) output weights obtained by online training. The sensitivity of the SNN to the neuron centers determined in the first step is evaluated on generators of different ratings and parameters. Performances of the SNN and MLP are compared to evaluate robustness on the identification of generator dynamics under small and large disturbances, and to illustrate that SNNs are capable of learning nonlinear dynamics of complex systems.

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

    Science.gov (United States)

    Tuckwell, Henry C; Penington, Nicholas J

    2014-07-01

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

  16. Bayesian Inference for Structured Spike and Slab Priors

    DEFF Research Database (Denmark)

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

    2014-01-01

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

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

    DEFF Research Database (Denmark)

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

    2009-01-01

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

  18. Spike-timing-based computation in sound localization.

    Directory of Open Access Journals (Sweden)

    Dan F M Goodman

    2010-11-01

    Full Text Available Spike timing is precise in the auditory system and it has been argued that it conveys information about auditory stimuli, in particular about the location of a sound source. However, beyond simple time differences, the way in which neurons might extract this information is unclear and the potential computational advantages are unknown. The computational difficulty of this task for an animal is to locate the source of an unexpected sound from two monaural signals that are highly dependent on the unknown source signal. In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. The model was able to accurately estimate the location of previously unknown sounds in both azimuth and elevation (including front/back discrimination in a known acoustic environment. We found that multiple representations of different acoustic environments could coexist as sets of overlapping neural assemblies which could be associated with spatial locations by Hebbian learning. The model demonstrates the computational relevance of relative spike timing to extract spatial information about sources independently of the source signal.

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

    NARCIS (Netherlands)

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

    1995-01-01

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

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

    Indian Academy of Sciences (India)

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

  1. Fast computation with spikes in a recurrent neural network

    International Nuclear Information System (INIS)

    Jin, Dezhe Z.; Seung, H. Sebastian

    2002-01-01

    Neural networks with recurrent connections are sometimes regarded as too slow at computation to serve as models of the brain. Here we analytically study a counterexample, a network consisting of N integrate-and-fire neurons with self excitation, all-to-all inhibition, instantaneous synaptic coupling, and constant external driving inputs. When the inhibition and/or excitation are large enough, the network performs a winner-take-all computation for all possible external inputs and initial states of the network. The computation is done very quickly: As soon as the winner spikes once, the computation is completed since no other neurons will spike. For some initial states, the winner is the first neuron to spike, and the computation is done at the first spike of the network. In general, there are M potential winners, corresponding to the top M external inputs. When the external inputs are close in magnitude, M tends to be larger. If M>1, the selection of the actual winner is strongly influenced by the initial states. If a special relation between the excitation and inhibition is satisfied, the network always selects the neuron with the maximum external input as the winner

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

    Science.gov (United States)

    Patel, Ashok; Kosko, Bart

    2005-01-01

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

  3. Effect of Rolandic Spikes on ADHD Impulsive Behavior

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2007-01-01

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

  4. Lateralization of spike and wave complexes produced by hallucinogenic compounds in the cat.

    Science.gov (United States)

    Contreras, C M; Dorantes, M E; Mexicano, G; Guzmán-Flores, C

    1986-06-01

    The ability of four hallucinogenic compounds--ketamine, phencyclidine, quipazine, and SKF-10 047--to produce some specific electrical pattern in portions of the limbic system and the hemispheric lateralization of such effects were studied in cats with permanently implanted electrodes. Electronic frequency and area integrators were used to analyze the results, and the percentage change in electrographic alterations was calculated. All compounds studied produced trains of spike and wave complexes in the cingulum, rapid discharges in the amygdala complex, and slow-wave synchronous activity in the septal nucleus. Those changes predominated in the left hemisphere. At small but hallucinatory concentrations of these drugs, the cortical EEG was not affected. Exploratory movements directed toward nonexistent objects, classified as hallucinatory-like behavior, appeared simultaneous with these changes in the EEG recordings. We concluded that there could exist a relationship between the appearance of spike and wave complexes in the limbic system without epileptic signs (twitching or myoclonus) and the presence of hallucinations, and that there is a left side hemispheric lateralization of the electrographic effects, viewing cerebral dominance phenomena as a functional and fluctuating state.

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

    Directory of Open Access Journals (Sweden)

    T. K. Charles

    2016-10-01

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

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

    Directory of Open Access Journals (Sweden)

    Christian Meisel

    2015-02-01

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

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

    Science.gov (United States)

    Gardner, Brian; Grüning, André

    2016-01-01

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

  8. Discrimination of communication vocalizations by single neurons and groups of neurons in the auditory midbrain.

    Science.gov (United States)

    Schneider, David M; Woolley, Sarah M N

    2010-06-01

    Many social animals including songbirds use communication vocalizations for individual recognition. The perception of vocalizations depends on the encoding of complex sounds by neurons in the ascending auditory system, each of which is tuned to a particular subset of acoustic features. Here, we examined how well the responses of single auditory neurons could be used to discriminate among bird songs and we compared discriminability to spectrotemporal tuning. We then used biologically realistic models of pooled neural responses to test whether the responses of groups of neurons discriminated among songs better than the responses of single neurons and whether discrimination by groups of neurons was related to spectrotemporal tuning and trial-to-trial response variability. The responses of single auditory midbrain neurons could be used to discriminate among vocalizations with a wide range of abilities, ranging from chance to 100%. The ability to discriminate among songs using single neuron responses was not correlated with spectrotemporal tuning. Pooling the responses of pairs of neurons generally led to better discrimination than the average of the two inputs and the most discriminating input. Pooling the responses of three to five single neurons continued to improve neural discrimination. The increase in discriminability was largest for groups of neurons with similar spectrotemporal tuning. Further, we found that groups of neurons with correlated spike trains achieved the largest gains in discriminability. We simulated neurons with varying levels of temporal precision and measured the discriminability of responses from single simulated neurons and groups of simulated neurons. Simulated neurons with biologically observed levels of temporal precision benefited more from pooling correlated inputs than did neurons with highly precise or imprecise spike trains. These findings suggest that pooling correlated neural responses with the levels of precision observed in the