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Sample records for spike detection preserves

  1. Automatic EEG spike detection.

    Science.gov (United States)

    Harner, Richard

    2009-10-01

    Since the 1970s advances in science and technology during each succeeding decade have renewed the expectation of efficient, reliable automatic epileptiform spike detection (AESD). But even when reinforced with better, faster tools, clinically reliable unsupervised spike detection remains beyond our reach. Expert-selected spike parameters were the first and still most widely used for AESD. Thresholds for amplitude, duration, sharpness, rise-time, fall-time, after-coming slow waves, background frequency, and more have been used. It is still unclear which of these wave parameters are essential, beyond peak-peak amplitude and duration. Wavelet parameters are very appropriate to AESD but need to be combined with other parameters to achieve desired levels of spike detection efficiency. Artificial Neural Network (ANN) and expert-system methods may have reached peak efficiency. Support Vector Machine (SVM) technology focuses on outliers rather than centroids of spike and nonspike data clusters and should improve AESD efficiency. An exemplary spike/nonspike database is suggested as a tool for assessing parameters and methods for AESD and is available in CSV or Matlab formats from the author at brainvue@gmail.com. Exploratory Data Analysis (EDA) is presented as a graphic method for finding better spike parameters and for the step-wise evaluation of the spike detection process.

  2. 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...

  3. Application of cross-correlated delay shift rule in spiking neural networks for interictal spike detection.

    Science.gov (United States)

    Lilin Guo; Zhenzhong Wang; Cabrerizo, Mercedes; Adjouadi, Malek

    2016-08-01

    This study proposes a Cross-Correlated Delay Shift (CCDS) supervised learning rule to train neurons with associated spatiotemporal patterns to classify spike patterns. The objective of this study was to evaluate the feasibility of using the CCDS rule to automate the detection of interictal spikes in electroencephalogram (EEG) data on patients with epilepsy. Encoding is the initial yet essential step for spiking neurons to process EEG patterns. A new encoding method is utilized to convert the EEG signal into spike patterns. The simulation results show that the proposed algorithm identified 69 spikes out of 82 spikes, or 84% detection rate, which is quite high considering the subtleties of interictal spikes and the tediousness of monitoring long EEG records. This CCDS rule is also benchmarked by ReSuMe on the same task.

  4. Detection of bursts in neuronal spike trains by the mean inter-spike interval method

    Institute of Scientific and Technical Information of China (English)

    Lin Chen; Yong Deng; Weihua Luo; Zhen Wang; Shaoqun Zeng

    2009-01-01

    Bursts are electrical spikes firing with a high frequency, which are the most important property in synaptic plasticity and information processing in the central nervous system. However, bursts are difficult to identify because bursting activities or patterns vary with phys-iological conditions or external stimuli. In this paper, a simple method automatically to detect bursts in spike trains is described. This method auto-adaptively sets a parameter (mean inter-spike interval) according to intrinsic properties of the detected burst spike trains, without any arbitrary choices or any operator judgrnent. When the mean value of several successive inter-spike intervals is not larger than the parameter, a burst is identified. By this method, bursts can be automatically extracted from different bursting patterns of cultured neurons on multi-electrode arrays, as accurately as by visual inspection. Furthermore, significant changes of burst variables caused by electrical stimulus have been found in spontaneous activity of neuronal network. These suggest that the mean inter-spike interval method is robust for detecting changes in burst patterns and characteristics induced by environmental alterations.

  5. Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering.

    Science.gov (United States)

    Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

    2014-12-30

    Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. An automatic unsupervised spike sorting method is proposed in this paper. The method uses features extracted by the locality preserving projection (LPP) algorithm. These features afterwards serve as inputs for the landmark-based spectral clustering (LSC) method. Gap statistics (GS) is employed to evaluate the number of clusters before the LSC can be performed. The proposed LPP-LSC is highly accurate and computationally inexpensive spike sorting approach. LPP spike features are very discriminative; thereby boost the performance of clustering methods. Furthermore, the LSC method exhibits its efficiency when integrated with the cluster evaluator GS. The proposed method's accuracy is approximately 13% superior to that of the benchmark combination between wavelet transformation and superparamagnetic clustering (WT-SPC). Additionally, LPP-LSC computing time is six times less than that of the WT-SPC. LPP-LSC obviously demonstrates a win-win spike sorting solution meeting both accuracy and computational cost criteria. LPP and LSC are linear algorithms that help reduce computational burden and thus their combination can be applied into real-time spike analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Coincidence Detection Using Spiking Neurons with Application to Face Recognition

    Directory of Open Access Journals (Sweden)

    Fadhlan Kamaruzaman

    2015-01-01

    Full Text Available We elucidate the practical implementation of Spiking Neural Network (SNN as local ensembles of classifiers. Synaptic time constant τs is used as learning parameter in representing the variations learned from a set of training data at classifier level. This classifier uses coincidence detection (CD strategy trained in supervised manner using a novel supervised learning method called τs Prediction which adjusts the precise timing of output spikes towards the desired spike timing through iterative adaptation of τs. This paper also discusses the approximation of spike timing in Spike Response Model (SRM for the purpose of coincidence detection. This process significantly speeds up the whole process of learning and classification. Performance evaluations with face datasets such as AR, FERET, JAFFE, and CK+ datasets show that the proposed method delivers better face classification performance than the network trained with Supervised Synaptic-Time Dependent Plasticity (STDP. We also found that the proposed method delivers better classification accuracy than k nearest neighbor, ensembles of kNN, and Support Vector Machines. Evaluation on several types of spike codings also reveals that latency coding delivers the best result for face classification as well as for classification of other multivariate datasets.

  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. Voltage spike detection in high field superconducting accelerator magnets

    Energy Technology Data Exchange (ETDEWEB)

    Orris, D.F.; Carcagno, R.; Feher, S.; Makulski, A.; Pischalnikov, Y.M.; /Fermilab

    2004-12-01

    A measurement system for the detection of small magnetic flux changes in superconducting magnets, which are due to either mechanical motion of the conductor or flux jump, has been developed at Fermilab. These flux changes are detected as small amplitude, short duration voltage spikes, which are {approx}15mV in magnitude and lasts for {approx}30 {micro}sec. The detection system combines an analog circuit for the signal conditioning of two coil segments and a fast data acquisition system for digitizing the results, performing threshold detection, and storing the resultant data. The design of the spike detection system along with the modeling results and noise analysis will be presented. Data from tests of high field Nb{sub 3}Sn magnets at currents up to {approx}20KA will also be shown.

  9. Voltage spike detection in high field superconducting accelerator magnets

    International Nuclear Information System (INIS)

    Orris, D.F.; Carcagno, R.; Feher, S.; Makulski, A.; Pischalnikov, Y.M.

    2004-01-01

    A measurement system for the detection of small magnetic flux changes in superconducting magnets, which are due to either mechanical motion of the conductor or flux jump, has been developed at Fermilab. These flux changes are detected as small amplitude, short duration voltage spikes, which are ∼15mV in magnitude and lasts for ∼30(micro)sec. The detection system combines an analog circuit for the signal conditioning of two coil segments and a fast data acquisition system for digitizing the results, performing threshold detection, and storing the resultant data. The design of the spike detection system along with the modeling results and noise analysis will be presented. Data from tests of high field Nb3Sn magnets at currents up to ∼20KA will also be shown

  10. The detection of intestinal spike activity on surface electroenterograms

    Energy Technology Data Exchange (ETDEWEB)

    Ye-Lin, Y; Garcia-Casado, J; Martinez-de-Juan, J L; Prats-Boluda, G [Instituto interuniversitario de investigacion en bioingenierIa y tecnologIa orientada al ser humano (I3BH), Universidad Politecnica de Valencia, Camino de Vera, s/n, Ed. 8E, Acceso N, 2a, planta 46022 Valencia (Spain); Ponce, J L [Department of Surgery, Hospital Universitario La Fe de Valencia, Avenida Campanar n0. 51, 46009 Valencia (Spain)], E-mail: yiye@eln.upv.es, E-mail: jgarciac@eln.upv.es, E-mail: jlmartinez@eln.upv.es, E-mail: geprabo@eln.upv.es, E-mail: drjlponce@ono.com

    2010-02-07

    Myoelectrical recording could provide an alternative technique for assessing intestinal motility, which is a topic of great interest in gastroenterology since many gastrointestinal disorders are associated with intestinal dysmotility. The pacemaker activity (slow wave, SW) of the electroenterogram (EEnG) has been detected in abdominal surface recordings, although the activity related to bowel contractions (spike bursts, SB) has to date only been detected in experimental models with artificially favored electrical conductivity. The aim of the present work was to assess the possibility of detecting SB activity in abdominal surface recordings under physiological conditions. For this purpose, 11 recording sessions of simultaneous internal and external myolectrical signals were conducted on conscious dogs. Signal analysis was carried out in the spectral domain. The results show that in periods of intestinal contractile activity, high-frequency components of EEnG signals can be detected on the abdominal surface in addition to SW activity. The energy between 2 and 20 Hz of the surface myoelectrical recording presented good correlation with the internal intestinal motility index (0.64 {+-} 0.10 for channel 1 and 0.57 {+-} 0.11 for channel 2). This suggests that SB activity can also be detected in canine surface EEnG recording.

  11. The detection of intestinal spike activity on surface electroenterograms

    International Nuclear Information System (INIS)

    Ye-Lin, Y; Garcia-Casado, J; Martinez-de-Juan, J L; Prats-Boluda, G; Ponce, J L

    2010-01-01

    Myoelectrical recording could provide an alternative technique for assessing intestinal motility, which is a topic of great interest in gastroenterology since many gastrointestinal disorders are associated with intestinal dysmotility. The pacemaker activity (slow wave, SW) of the electroenterogram (EEnG) has been detected in abdominal surface recordings, although the activity related to bowel contractions (spike bursts, SB) has to date only been detected in experimental models with artificially favored electrical conductivity. The aim of the present work was to assess the possibility of detecting SB activity in abdominal surface recordings under physiological conditions. For this purpose, 11 recording sessions of simultaneous internal and external myolectrical signals were conducted on conscious dogs. Signal analysis was carried out in the spectral domain. The results show that in periods of intestinal contractile activity, high-frequency components of EEnG signals can be detected on the abdominal surface in addition to SW activity. The energy between 2 and 20 Hz of the surface myoelectrical recording presented good correlation with the internal intestinal motility index (0.64 ± 0.10 for channel 1 and 0.57 ± 0.11 for channel 2). This suggests that SB activity can also be detected in canine surface EEnG recording.

  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. Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

    Directory of Open Access Journals (Sweden)

    Pietro Quaglio

    2017-05-01

    Full Text Available Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs. STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons. In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST. We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE analysis.

  14. 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.

  15. Manifold structure preservative for hyperspectral target detection

    Science.gov (United States)

    Imani, Maryam

    2018-05-01

    A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.

  16. Multichannel interictal spike activity detection using time-frequency entropy measure.

    Science.gov (United States)

    Thanaraj, Palani; Parvathavarthini, B

    2017-06-01

    Localization of interictal spikes is an important clinical step in the pre-surgical assessment of pharmacoresistant epileptic patients. The manual selection of interictal spike periods is cumbersome and involves a considerable amount of analysis workload for the physician. The primary focus of this paper is to automate the detection of interictal spikes for clinical applications in epilepsy localization. The epilepsy localization procedure involves detection of spikes in a multichannel EEG epoch. Therefore, a multichannel Time-Frequency (T-F) entropy measure is proposed to extract features related to the interictal spike activity. Least squares support vector machine is used to train the proposed feature to classify the EEG epochs as either normal or interictal spike period. The proposed T-F entropy measure, when validated with epilepsy dataset of 15 patients, shows an interictal spike classification accuracy of 91.20%, sensitivity of 100% and specificity of 84.23%. Moreover, the area under the curve of Receiver Operating Characteristics plot of 0.9339 shows the superior classification performance of the proposed T-F entropy measure. The results of this paper show a good spike detection accuracy without any prior information about the spike morphology.

  17. Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

    Science.gov (United States)

    Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

    2018-06-01

    Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

  18. A 16-Channel Nonparametric Spike Detection ASIC Based on EC-PC Decomposition.

    Science.gov (United States)

    Wu, Tong; Xu, Jian; Lian, Yong; Khalili, Azam; Rastegarnia, Amir; Guan, Cuntai; Yang, Zhi

    2016-02-01

    In extracellular neural recording experiments, detecting neural spikes is an important step for reliable information decoding. A successful implementation in integrated circuits can achieve substantial data volume reduction, potentially enabling a wireless operation and closed-loop system. In this paper, we report a 16-channel neural spike detection chip based on a customized spike detection method named as exponential component-polynomial component (EC-PC) algorithm. This algorithm features a reliable prediction of spikes by applying a probability threshold. The chip takes raw data as input and outputs three data streams simultaneously: field potentials, band-pass filtered neural data, and spiking probability maps. The algorithm parameters are on-chip configured automatically based on input data, which avoids manual parameter tuning. The chip has been tested with both in vivo experiments for functional verification and bench-top experiments for quantitative performance assessment. The system has a total power consumption of 1.36 mW and occupies an area of 6.71 mm (2) for 16 channels. When tested on synthesized datasets with spikes and noise segments extracted from in vivo preparations and scaled according to required precisions, the chip outperforms other detectors. A credit card sized prototype board is developed to provide power and data management through a USB port.

  19. On the robustness of EC-PC spike detection method for online neural recording.

    Science.gov (United States)

    Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

    2014-09-30

    Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

  20. Development and Validation of a Spike Detection and Classification Algorithm Aimed at Implementation on Hardware Devices

    Directory of Open Access Journals (Sweden)

    E. Biffi

    2010-01-01

    Full Text Available Neurons cultured in vitro on MicroElectrode Array (MEA devices connect to each other, forming a network. To study electrophysiological activity and long term plasticity effects, long period recording and spike sorter methods are needed. Therefore, on-line and real time analysis, optimization of memory use and data transmission rate improvement become necessary. We developed an algorithm for amplitude-threshold spikes detection, whose performances were verified with (a statistical analysis on both simulated and real signal and (b Big O Notation. Moreover, we developed a PCA-hierarchical classifier, evaluated on simulated and real signal. Finally we proposed a spike detection hardware design on FPGA, whose feasibility was verified in terms of CLBs number, memory occupation and temporal requirements; once realized, it will be able to execute on-line detection and real time waveform analysis, reducing data storage problems.

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

    Science.gov (United States)

    Schafer, Phillip B; Jin, Dezhe Z

    2014-03-01

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

  2. Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains.

    Science.gov (United States)

    Pillow, Jonathan W; Ahmadian, Yashar; Paninski, Liam

    2011-01-01

    One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.

  3. Detection of Foodborne Pathogenic Bacteria using Bacteriophage Tail Spike Proteins

    Science.gov (United States)

    Poshtiban, Somayyeh

    Foodborne infections are worldwide health problem with tremendous social and financial impacts. Efforts are focused on developing accurate and reliable technologies for detection of food contaminations in early stages preferably on-site. This thesis focuses on interfacing engineering and biology by combining phage receptor binding proteins (RBPs) with engineered platforms including microresonator-based biosensors, magnetic particles and polymerase chain reaction (PCR) to develop bacterial detection sensors. We used phage RBPs as target specific bioreceptors to develop an enhanced microresonator array for bacterial detection. These resonator beams are optimized to feature a high natural frequency while offer large surface area for capture of bacteria. Theoretical analysis indicates a high mass sensitivity with a threshold for the detection of a single bacterial cell. We used phage RBPs as target specific bioreceptors, and successfully demonstrated the application of these phage RBB-immobilized arrays for specific detection of C. jejuni cells. We also developed a RBP-derivatized magnetic pre-enrichment method as an upstream sample preparation method to improve sensitivity and specificity of PCR for detection of bacterial cells in various food samples. The combination of RBP-based magnetic separation and real-time PCR allowed the detection of small number of bacteria in artificially contaminated food samples without any need for time consuming pre-enrichment step through culturing. We also looked into integration of the RBP-based magnetic separation with PCR onto a single microfluidic lab-on-a-chip to reduce the overall turnaround time.

  4. Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains

    Science.gov (United States)

    Malvestio, Irene; Kreuz, Thomas; Andrzejak, Ralph G.

    2017-08-01

    The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L . Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes. We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data.

  5. Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation.

    Science.gov (United States)

    Azami, Hamed; Escudero, Javier

    2016-05-01

    Signal segmentation and spike detection are two important biomedical signal processing applications. Often, non-stationary signals must be segmented into piece-wise stationary epochs or spikes need to be found among a background of noise before being further analyzed. Permutation entropy (PE) has been proposed to evaluate the irregularity of a time series. PE is conceptually simple, structurally robust to artifacts, and computationally fast. It has been extensively used in many applications, but it has two key shortcomings. First, when a signal is symbolized using the Bandt-Pompe procedure, only the order of the amplitude values is considered and information regarding the amplitudes is discarded. Second, in the PE, the effect of equal amplitude values in each embedded vector is not addressed. To address these issues, we propose a new entropy measure based on PE: the amplitude-aware permutation entropy (AAPE). AAPE is sensitive to the changes in the amplitude, in addition to the frequency, of the signals thanks to it being more flexible than the classical PE in the quantification of the signal motifs. To demonstrate how the AAPE method can enhance the quality of the signal segmentation and spike detection, a set of synthetic and realistic synthetic neuronal signals, electroencephalograms and neuronal data are processed. We compare the performance of AAPE in these problems against state-of-the-art approaches and evaluate the significance of the differences with a repeated ANOVA with post hoc Tukey's test. In signal segmentation, the accuracy of AAPE-based method is higher than conventional segmentation methods. AAPE also leads to more robust results in the presence of noise. The spike detection results show that AAPE can detect spikes well, even when presented with single-sample spikes, unlike PE. For multi-sample spikes, the changes in AAPE are larger than in PE. We introduce a new entropy metric, AAPE, that enables us to consider amplitude information in the

  6. Google Searches for "Cheap Cigarettes" Spike at Tax Increases: Evidence from an Algorithm to Detect Spikes in Time Series Data.

    Science.gov (United States)

    Caputi, Theodore L

    2018-05-03

    Online cigarette dealers have lower prices than brick-and-mortar retailers and advertise tax-free status.1-8 Previous studies show smokers search out these online alternatives at the time of a cigarette tax increase.9,10 However, these studies rely upon researchers' decision to consider a specific date and preclude the possibility that researchers focus on the wrong date. The purpose of this study is to introduce an unbiased methodology to the field of observing search patterns and to use this methodology to determine whether smokers search Google for "cheap cigarettes" at cigarette tax increases and, if so, whether the increased level of searches persists. Publicly available data from Google Trends is used to observe standardized search volumes for the term, "cheap cigarettes". Seasonal Hybrid Extreme Studentized Deviate and E-Divisive with Means tests were performed to observe spikes and mean level shifts in search volume. Of the twelve cigarette tax increases studied, ten showed spikes in searches for "cheap cigarettes" within two weeks of the tax increase. However, the mean level shifts did not occur for any cigarette tax increase. Searches for "cheap cigarettes" spike around the time of a cigarette tax increase, but the mean level of searches does not shift in response to a tax increase. The SHESD and EDM tests are unbiased methodologies that can be used to identify spikes and mean level shifts in time series data without an a priori date to be studied. SHESD and EDM affirm spikes in interest are related to tax increases. • Applies improved statistical techniques (SHESD and EDM) to Google search data related to cigarettes, reducing bias and increasing power • Contributes to the body of evidence that state and federal tax increases are associated with spikes in searches for cheap cigarettes and may be good dates for increased online health messaging related to tobacco.

  7. A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data.

    Science.gov (United States)

    Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

    2017-06-01

    Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

  8. A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

    Science.gov (United States)

    Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

    2017-06-01

    Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

  9. Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

    Directory of Open Access Journals (Sweden)

    Turky N. Alotaiby

    2017-01-01

    Full Text Available Epilepsy is a neurological disorder that affects millions of people worldwide. Monitoring the brain activities and identifying the seizure source which starts with spike detection are important steps for epilepsy treatment. Magnetoencephalography (MEG is an emerging epileptic diagnostic tool with high-density sensors; this makes manual analysis a challenging task due to the vast amount of MEG data. This paper explores the use of eight statistical features and genetic programing (GP with the K-nearest neighbor (KNN for interictal spike detection. The proposed method is comprised of three stages: preprocessing, genetic programming-based feature generation, and classification. The effectiveness of the proposed approach has been evaluated using real MEG data obtained from 28 epileptic patients. It has achieved a 91.75% average sensitivity and 92.99% average specificity.

  10. Signal post-processing for acoustic velocimeters: detecting and replacing spikes

    International Nuclear Information System (INIS)

    Razaz, Mahdi; Kawanisi, Kiyosi

    2011-01-01

    Time series recorded by acoustic velocimeters are often affected by a combination of factors, including turbulent velocity fluctuations, Doppler noise and signal aliasing. Although it is not possible to find a comprehensive threshold for identifying spurious data, the present work attempts to describe an effective technique for detecting spikes. This technique is based on transforming data into wavelet space and thresholding the wavelet basis by a consistent threshold. The universal threshold modified by a robust scale estimator such as Q n is proven to work extremely well. The suggested methods for replacing identified spikes combine times series analyses (linear time series modelling or a Kalman predictor) with a straightforward method, polynomial interpolation, to generate substitutions retaining both the trends and the fluctuations in the surrounding clean data. Then, tests were performed to reveal the influence of replacing methods on the total number of detected spikes, required iterations and physical properties of the restored signal. From the overall results, it is inferred that using the wavelet-Q n as the detecting module and integrating it with linear time series modelling/Kalman filtering as the replacement module constitutes an effective despiking algorithm. This methodology is capable of restoring the contaminated signal in such a way that its statistical and physical properties correlate well with those of the original record

  11. Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks.

    Science.gov (United States)

    Martens, Marijn B; Houweling, Arthur R; E Tiesinga, Paul H

    2017-02-01

    Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution.

  12. 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.

  13. Detection of M-Sequences from Spike Sequence in Neuronal Networks

    Directory of Open Access Journals (Sweden)

    Yoshi Nishitani

    2012-01-01

    Full Text Available In circuit theory, it is well known that a linear feedback shift register (LFSR circuit generates pseudorandom bit sequences (PRBS, including an M-sequence with the maximum period of length. In this study, we tried to detect M-sequences known as a pseudorandom sequence generated by the LFSR circuit from time series patterns of stimulated action potentials. Stimulated action potentials were recorded from dissociated cultures of hippocampal neurons grown on a multielectrode array. We could find several M-sequences from a 3-stage LFSR circuit (M3. These results show the possibility of assembling LFSR circuits or its equivalent ones in a neuronal network. However, since the M3 pattern was composed of only four spike intervals, the possibility of an accidental detection was not zero. Then, we detected M-sequences from random spike sequences which were not generated from an LFSR circuit and compare the result with the number of M-sequences from the originally observed raster data. As a result, a significant difference was confirmed: a greater number of “0–1” reversed the 3-stage M-sequences occurred than would have accidentally be detected. This result suggests that some LFSR equivalent circuits are assembled in neuronal networks.

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

    Science.gov (United States)

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

    2017-01-01

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

  15. [Detection of the preservative chlorphenesin in cosmetics by high-performance liquid chromatography].

    Science.gov (United States)

    Ikarashi, Yoshiaki; Miyazawa, Norimasa; Shimamura, Kimio; Sato, Nobuo; Yoshizawa, Ken-ichi; Hayashi, Masahito; Takano, Katsuhiro; Miyamoto, Michiko; Kojima, Takashi; Sakaguchi, Hiroshi; Fujiio, Makiko

    2009-01-01

    A simple determination method for preservative chlorphenesin in cosmetics was developed. Cosmetic samples were dissolved in methanol. The sample solution was analyzed by high-performance liquid chromatography (HPLC) with ODS column, using water-methanol (55:45) or water-acetonitrile (3:1) adjusted to pH 2.5 with phosphoric acid as the mobile phase. Chlorphenesin was detected with ultraviolet light detection at 280 nm. A linear relation was obtained between the peak areas and the concentrations of chlorphenesin in the range of 1-500 microg/ml. The determination limit of chlorphenesin was 1-2 microg/ml. Recoveries of chlorphenesin spiked in lotion and milky lotion at the levels of 0.03% and 0.3% were 98.8-100.0%. This method was applied for cosmetics including 0.03% and 0.3% of chlorphenesin and their content corresponded with the determined values.

  16. Highly Sensitive Bacteriophage-Based Detection of Brucella abortus in Mixed Culture and Spiked Blood

    Directory of Open Access Journals (Sweden)

    Kirill V. Sergueev

    2017-06-01

    Full Text Available For decades, bacteriophages (phages have been used for Brucella species identification in the diagnosis and epidemiology of brucellosis. Traditional Brucella phage typing is a multi-day procedure including the isolation of a pure culture, a step that can take up to three weeks. In this study, we focused on the use of brucellaphages for sensitive detection of the pathogen in clinical and other complex samples, and developed an indirect method of Brucella detection using real-time quantitative PCR monitoring of brucellaphage DNA amplification via replication on live Brucella cells. This assay allowed the detection of single bacteria (down to 1 colony-forming unit per milliliter within 72 h without DNA extraction and purification steps. The technique was equally efficient with Brucella abortus pure culture and with mixed cultures of B. abortus and α-proteobacterial near neighbors that can be misidentified as Brucella spp., Ochrobactrum anthropi and Afipia felis. The addition of a simple short sample preparation step enabled the indirect phage-based detection of B. abortus in spiked blood, with the same high sensitivity. This indirect phage-based detection assay enables the rapid and sensitive detection of live B. abortus in mixed cultures and in blood samples, and can potentially be applied for detection in other clinical samples and other complex sample types.

  17. Highly Sensitive Bacteriophage-Based Detection of Brucella abortus in Mixed Culture and Spiked Blood.

    Science.gov (United States)

    Sergueev, Kirill V; Filippov, Andrey A; Nikolich, Mikeljon P

    2017-06-10

    For decades, bacteriophages (phages) have been used for Brucella species identification in the diagnosis and epidemiology of brucellosis. Traditional Brucella phage typing is a multi-day procedure including the isolation of a pure culture, a step that can take up to three weeks. In this study, we focused on the use of brucellaphages for sensitive detection of the pathogen in clinical and other complex samples, and developed an indirect method of Brucella detection using real-time quantitative PCR monitoring of brucellaphage DNA amplification via replication on live Brucella cells. This assay allowed the detection of single bacteria (down to 1 colony-forming unit per milliliter) within 72 h without DNA extraction and purification steps. The technique was equally efficient with Brucella abortus pure culture and with mixed cultures of B . abortus and α-proteobacterial near neighbors that can be misidentified as Brucella spp., Ochrobactrum anthropi and Afipia felis . The addition of a simple short sample preparation step enabled the indirect phage-based detection of B . abortus in spiked blood, with the same high sensitivity. This indirect phage-based detection assay enables the rapid and sensitive detection of live B . abortus in mixed cultures and in blood samples, and can potentially be applied for detection in other clinical samples and other complex sample types.

  18. Highly Sensitive Bacteriophage-Based Detection of Brucella abortus in Mixed Culture and Spiked Blood

    Science.gov (United States)

    Sergueev, Kirill V.; Filippov, Andrey A.; Nikolich, Mikeljon P.

    2017-01-01

    For decades, bacteriophages (phages) have been used for Brucella species identification in the diagnosis and epidemiology of brucellosis. Traditional Brucella phage typing is a multi-day procedure including the isolation of a pure culture, a step that can take up to three weeks. In this study, we focused on the use of brucellaphages for sensitive detection of the pathogen in clinical and other complex samples, and developed an indirect method of Brucella detection using real-time quantitative PCR monitoring of brucellaphage DNA amplification via replication on live Brucella cells. This assay allowed the detection of single bacteria (down to 1 colony-forming unit per milliliter) within 72 h without DNA extraction and purification steps. The technique was equally efficient with Brucella abortus pure culture and with mixed cultures of B. abortus and α-proteobacterial near neighbors that can be misidentified as Brucella spp., Ochrobactrum anthropi and Afipia felis. The addition of a simple short sample preparation step enabled the indirect phage-based detection of B. abortus in spiked blood, with the same high sensitivity. This indirect phage-based detection assay enables the rapid and sensitive detection of live B. abortus in mixed cultures and in blood samples, and can potentially be applied for detection in other clinical samples and other complex sample types. PMID:28604602

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

    Science.gov (United States)

    Goense, J B M; Ratnam, R

    2003-10-01

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

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

    Science.gov (United States)

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

    2010-03-01

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

  1. A Path Tracking Algorithm Using Future Prediction Control with Spike Detection for an Autonomous Vehicle Robot

    Directory of Open Access Journals (Sweden)

    Muhammad Aizzat Zakaria

    2013-08-01

    Full Text Available Trajectory tracking is an important aspect of autonomous vehicles. The idea behind trajectory tracking is the ability of the vehicle to follow a predefined path with zero steady state error. The difficulty arises due to the nonlinearity of vehicle dynamics. Therefore, this paper proposes a stable tracking control for an autonomous vehicle. An approach that consists of steering wheel control and lateral control is introduced. This control algorithm is used for a non-holonomic navigation problem, namely tracking a reference trajectory in a closed loop form. A proposed future prediction point control algorithm is used to calculate the vehicle's lateral error in order to improve the performance of the trajectory tracking. A feedback sensor signal from the steering wheel angle and yaw rate sensor is used as feedback information for the controller. The controller consists of a relationship between the future point lateral error, the linear velocity, the heading error and the reference yaw rate. This paper also introduces a spike detection algorithm to track the spike error that occurs during GPS reading. The proposed idea is to take the advantage of the derivative of the steering rate. This paper aims to tackle the lateral error problem by applying the steering control law to the vehicle, and proposes a new path tracking control method by considering the future coordinate of the vehicle and the future estimated lateral error. The effectiveness of the proposed controller is demonstrated by a simulation and a GPS experiment with noisy data. The approach used in this paper is not limited to autonomous vehicles alone since the concept of autonomous vehicle tracking can be used in mobile robot platforms, as the kinematic model of these two platforms is similar.

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

    DEFF Research Database (Denmark)

    Sacerdote, Laura; Tamborrino, Massimiliano; Zucca, Cristina

    2011-01-01

    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...... the two neurons. Furthermore, the method recognizes the presence of delays in the spike propagation....

  3. EPR detection of foods preserved with ionizing radiation

    Science.gov (United States)

    Stachowicz, W.; Burlinska, G.; Michalik, J.

    1998-06-01

    The applicability of the epr technique for the detection of dried vegetables, mushrooms, some spices, flavour additives and some condiments preserved with ionizing radiation is discussed. The epr signals recorded after exposure to gamma rays and to beams of 10 MeV electrons from linac are stable, intense and specific enough as compared with those observed with nonirradiated samples and could be used for the detection of irradiation. However, stability of radiation induced epr signals produced in these foods depends on storage condition. No differences in shapes (spectral parameters) and intensities of the epr spectra recorded with samples exposed to the same doses of gamma rays ( 60Co) and 10 MeV electrons were observed

  4. EPR detection of foods preserved with ionizing radiation

    International Nuclear Information System (INIS)

    Stachowicz, W.; Burlinska, G.; Michalik, J.

    1998-01-01

    The applicability of the epr technique for the detection of dried vegetables, mushrooms, some spices, flavour additives and some condiments preserved with ionizing radiation is discussed. The epr signals recorded after exposure to gamma rays and to the beams of 10 MeV electrons from linac are stable, intense and specific enough as compared with those observed with nonirradiated samples and could be used for the detection of irradiation. However, stability of radiation induced epr signals produced in these foods depends on storage condition. No differences in shapes (spectral parameters) and intensities of the epr spectra recorded with samples exposed to the same doses of gamma rays ( 60 Co) and 10 MeV electrons were observed

  5. EPR detection of foods preserved with ionizing radiation

    Energy Technology Data Exchange (ETDEWEB)

    Stachowicz, W.; Burlinska, G.; Michalik, J

    1998-06-01

    The applicability of the epr technique for the detection of dried vegetables, mushrooms, some spices, flavour additives and some condiments preserved with ionizing radiation is discussed. The epr signals recorded after exposure to gamma rays and to the beams of 10 MeV electrons from linac are stable, intense and specific enough as compared with those observed with nonirradiated samples and could be used for the detection of irradiation. However, stability of radiation induced epr signals produced in these foods depends on storage condition. No differences in shapes (spectral parameters) and intensities of the epr spectra recorded with samples exposed to the same doses of gamma rays ({sup 60}Co) and 10 MeV electrons were observed.

  6. Saccadic spike potentials in gamma-band EEG: characterization, detection and suppression.

    Science.gov (United States)

    Keren, Alon S; Yuval-Greenberg, Shlomit; Deouell, Leon Y

    2010-02-01

    Analysis of high-frequency (gamma-band) neural activity by means of non-invasive EEG is gaining increasing interest. However, we have recently shown that a saccade-related spike potential (SP) seriously confounds the analysis of EEG induced gamma-band responses (iGBR), as the SP eludes traditional EEG artifact rejection methods. Here we provide a comprehensive profile of the SP and evaluate methods for its detection and suppression, aiming to unveil true cerebral gamma-band activity. The SP appears consistently as a sharp biphasic deflection of about 22 ms starting at the saccade onset, with a frequency band of approximately 20-90 Hz. On the average, larger saccades elicit higher SP amplitudes. The SP amplitude gradually changes from the extra-ocular channels towards posterior sites with the steepest gradients around the eyes, indicating its ocular source. Although the amplitude and the sign of the SP depend on the choice of reference channel, the potential gradients remain the same and non-zero for all references. The scalp topography is modulated almost exclusively by the direction of saccades, with steeper gradients ipsilateral to the saccade target. We discuss how the above characteristics impede attempts to remove these SPs from the EEG by common temporal filtering, choice of different references, or rejection of contaminated trials. We examine the extent to which SPs can be reliably detected without an eye tracker, assess the degree to which scalp current density derivation attenuates the effect of the SP, and propose a tailored ICA procedure for minimizing the effect of the SP. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  7. A comparative analysis of preservation techniques for the optimal molecular detection of hookworm DNA in a human fecal specimen

    Science.gov (United States)

    Pilotte, Nils; Baumer, Ben; Grant, Jessica; Asbjornsdottir, Kristjana; Schaer, Fabian; Hu, Yan; Aroian, Raffi; Walson, Judd; Williams, Steven A.

    2018-01-01

    Background Proper collection and storage of fecal samples is necessary to guarantee the subsequent reliability of DNA-based soil-transmitted helminth diagnostic procedures. Previous research has examined various methods to preserve fecal samples for subsequent microscopic analysis or for subsequent determination of overall DNA yields obtained following DNA extraction. However, only limited research has focused on the preservation of soil-transmitted helminth DNA in stool samples stored at ambient temperature or maintained in a cold chain for extended periods of time. Methodology Quantitative real-time PCR was used in this study as a measure of the effectiveness of seven commercially available products to preserve hookworm DNA over time and at different temperatures. Results were compared against “no preservative” controls and the “gold standard” of rapidly freezing samples at -20°C. The preservation methods were compared at both 4°C and at simulated tropical ambient temperature (32°C) over a period of 60 days. Evaluation of the effectiveness of each preservative was based on quantitative real-time PCR detection of target hookworm DNA. Conclusions At 4°C there were no significant differences in DNA amplification efficiency (as measured by Cq values) regardless of the preservation method utilized over the 60-day period. At 32°C, preservation with FTA cards, potassium dichromate, and a silica bead two-step desiccation process proved most advantageous for minimizing Cq value increases, while RNA later, 95% ethanol and Paxgene also demonstrate some protective effect. These results suggest that fecal samples spiked with known concentrations of hookworm-derived egg material can remain at 4°C for 60 days in the absence of preservative, without significant degradation of the DNA target. Likewise, a variety of preservation methods can provide a measure of protection in the absence of a cold chain. As a result, other factors, such as preservative toxicity

  8. Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection.

    Science.gov (United States)

    Son, Junggab; Park, Juyoung; Oh, Heekuck; Bhuiyan, Md Zakirul Alam; Hur, Junbeom; Kang, Kyungtae

    2017-06-12

    Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan-Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities.

  9. Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection

    Science.gov (United States)

    Son, Junggab; Park, Juyoung; Oh, Heekuck; Bhuiyan, Md Zakirul Alam; Hur, Junbeom; Kang, Kyungtae

    2017-01-01

    Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan–Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities. PMID:28604628

  10. Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks

    NARCIS (Netherlands)

    Martens, M.B. (Marijn B.); A.R. Houweling (Arthur); E. Tiesinga, P.H. (Paul H.)

    2017-01-01

    textabstractNeuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide

  11. Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

    Science.gov (United States)

    Mena, Gonzalo E; Grosberg, Lauren E; Madugula, Sasidhar; Hottowy, Paweł; Litke, Alan; Cunningham, John; Chichilnisky, E J; Paninski, Liam

    2017-11-01

    Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.

  12. Comparison of DNA extraction kits for detection of Burkholderia pseudomallei in spiked human whole blood using real-time PCR.

    Directory of Open Access Journals (Sweden)

    Nicole L Podnecky

    Full Text Available Burkholderia pseudomallei, the etiologic agent of melioidosis, is endemic in northern Australia and Southeast Asia and can cause severe septicemia that may lead to death in 20% to 50% of cases. Rapid detection of B. pseudomallei infection is crucial for timely treatment of septic patients. This study evaluated seven commercially available DNA extraction kits to determine the relative recovery of B. pseudomallei DNA from spiked EDTA-containing human whole blood. The evaluation included three manual kits: the QIAamp DNA Mini kit, the QIAamp DNA Blood Mini kit, and the High Pure PCR Template Preparation kit; and four automated systems: the MagNAPure LC using the DNA Isolation Kit I, the MagNAPure Compact using the Nucleic Acid Isolation Kit I, and the QIAcube using the QIAamp DNA Mini kit and the QIAamp DNA Blood Mini kit. Detection of B. pseudomallei DNA extracted by each kit was performed using the B. pseudomallei specific type III secretion real-time PCR (TTS1 assay. Crossing threshold (C T values were used to compare the limit of detection and reproducibility of each kit. This study also compared the DNA concentrations and DNA purity yielded for each kit. The following kits consistently yielded DNA that produced a detectable signal from blood spiked with 5.5×10(4 colony forming units per mL: the High Pure PCR Template Preparation, QIAamp DNA Mini, MagNA Pure Compact, and the QIAcube running the QIAamp DNA Mini and QIAamp DNA Blood Mini kits. The High Pure PCR Template Preparation kit yielded the lowest limit of detection with spiked blood, but when this kit was used with blood from patients with confirmed cases of melioidosis, the bacteria was not reliably detected indicating blood may not be an optimal specimen.

  13. Detection of feline coronavirus spike gene mutations as a tool to diagnose feline infectious peritonitis.

    Science.gov (United States)

    Felten, Sandra; Weider, Karola; Doenges, Stephanie; Gruendl, Stefanie; Matiasek, Kaspar; Hermanns, Walter; Mueller, Elisabeth; Matiasek, Lara; Fischer, Andrea; Weber, Karin; Hirschberger, Johannes; Wess, Gerhard; Hartmann, Katrin

    2017-04-01

    Objectives Feline infectious peritonitis (FIP) is an important cause of death in the cat population worldwide. The ante-mortem diagnosis of FIP in clinical cases is still challenging. In cats without effusion, a definitive diagnosis can only be achieved post mortem or with invasive methods. The aim of this study was to evaluate the use of a combined reverse transcriptase nested polymerase chain reaction (RT-nPCR) and sequencing approach in the diagnosis of FIP, detecting mutations at two different nucleotide positions within the spike (S) gene. Methods The study population consisted of 64 cats with confirmed FIP and 63 cats in which FIP was initially suspected due to similar clinical or laboratory signs, but that were definitively diagnosed with another disease. Serum/plasma and/or effusion samples of these cats were examined for feline coronavirus (FCoV) RNA by RT-nPCR and, if positive, PCR products were sequenced for nucleotide transitions within the S gene. Results Specificity of RT-nPCR was 100% in all materials (95% confidence interval [CI] in serum/plasma 83.9-100.0; 95% CI in effusion 93.0-100.0). The specificity of the sequencing step could not be determined as none of the cats of the control group tested positive for FCoV RNA. Sensitivity of the 'combined RT-nPCR and sequencing approach' was 6.5% (95% CI 0.8-21.4) in serum/plasma and 65.3% (95% CI 50.4-78.3) in effusion. Conclusions and relevance A positive result is highly indicative of the presence of FIP, but as none of the control cats tested positive by RT-nPCR, it was not possible to confirm that the FCoV mutant described can only be found in cats with FIP. Further studies are necessary to evaluate the usefulness of the sequencing step including FCoV-RNA-positive cats with and without FIP. A negative result cannot be used to exclude the disease, especially when only serum/plasma samples are available.

  14. The Preservation and Detection of Organic Matter within Jarosite

    Science.gov (United States)

    Lewis, J. M. T.; Eigenbrode, J. L.; McAdam, A.; Andrejkovicova, S. C.; Knudson, C. A.; Wong, G. M.; Millan, M.; Freissinet, C.; Szopa, C.; Li, X.; Bower, D. M.

    2017-12-01

    Since its arrival at Mt. Sharp in 2014 the Mars Science Laboratory Curiosity rover has been examining the mountain's lower stratigraphy, which shows a progression from clay-bearing to sulfate-bearing strata. Clay minerals are known to be effective long-term preservers of organic matter [1], but it is important to also consider the potential for Martian sulfate minerals to host organic molecules. The Sample Analysis at Mars (SAM) instrument suite on board the rover uses pyrolysis to liberate organic fragments from sampled materials [2]. However, the surface of Mars hosts widespread oxychlorine phases, which thermally decompose to release oxygen and chlorine that can degrade and destroy organic signals [3]. Francois et al. (2016) demonstrated that synthetic magnesium sulfate can incorporate phthalic acid and protect it from oxychlorine phases during pyrolysis [4]. Magnesium sulfate as well as calcium sulfate and jarosite have all been observed by instruments on the rover. The addition of organic standards to the starting materials in jarosite synthesis reactions has conclusively demonstrated that jarosite can incorporate organic molecules. The samples were analyzed by SAM-like evolved gas analysis (EGA) and gas chromatography-mass spectrometry (GC-MS) and the influence of perchlorates assessed. Jarosite has been observed by multiple missions to the Martian surface and from orbit, thus the probability of future organic detection missions encountering the mineral is high. Samples from this study were examined by laser desorption/ionization mass spectrometry and Raman spectroscopy, which will be utilized by the ExoMars rover and Mars 2020 rover respectively. The data inform the sampling and analysis strategies for sulfate-rich regions of Mars for present and future organic-detection missions. [1] Farmer & Des Marais (1999) JGR: Planets 104, [2] Mahaffy et al., (2012) Space Science Reviews 170 [3] Glavin et al., (2013) JGR: Planets 118 [4] Francois et al., (2016) JGR

  15. Neural network based pattern matching and spike detection tools and services--in the CARMEN neuroinformatics project.

    Science.gov (United States)

    Fletcher, Martyn; Liang, Bojian; Smith, Leslie; Knowles, Alastair; Jackson, Tom; Jessop, Mark; Austin, Jim

    2008-10-01

    In the study of information flow in the nervous system, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services. Central to the CARMEN concept are federated CARMEN nodes, which provide: data and metadata storage, new, thirdparty and legacy services, and tools. In this paper, we describe the CARMEN project as well as the node infrastructure and an associated thick client tool for pattern visualisation and searching, the Signal Data Explorer (SDE). We also discuss new spike detection methods, which are central to the services provided by CARMEN. The SDE is a client application which can be used to explore data in the CARMEN repository, providing data visualization, signal processing and a pattern matching capability. It performs extremely fast pattern matching and can be used to search for complex conditions composed of many different patterns across the large datasets that are typical in neuroinformatics. Searches can also be constrained by specifying text based metadata filters. Spike detection services which use wavelet and morphology techniques are discussed, and have been shown to outperform traditional thresholding and template based systems. A number of different spike detection and sorting techniques will be deployed as services within the CARMEN infrastructure, to allow users to benchmark their performance against a wide range of reference datasets.

  16. Noise/spike detection in phonocardiogram signal as a cyclic random process with non-stationary period interval.

    Science.gov (United States)

    Naseri, H; Homaeinezhad, M R; Pourkhajeh, H

    2013-09-01

    The major aim of this study is to describe a unified procedure for detecting noisy segments and spikes in transduced signals with a cyclic but non-stationary periodic nature. According to this procedure, the cycles of the signal (onset and offset locations) are detected. Then, the cycles are clustered into a finite number of groups based on appropriate geometrical- and frequency-based time series. Next, the median template of each time series of each cluster is calculated. Afterwards, a correlation-based technique is devised for making a comparison between a test cycle feature and the associated time series of each cluster. Finally, by applying a suitably chosen threshold for the calculated correlation values, a segment is prescribed to be either clean or noisy. As a key merit of this research, the procedure can introduce a decision support for choosing accurately orthogonal-expansion-based filtering or to remove noisy segments. In this paper, the application procedure of the proposed method is comprehensively described by applying it to phonocardiogram (PCG) signals for finding noisy cycles. The database consists of 126 records from several patients of a domestic research station acquired by a 3M Littmann(®) 3200, 4KHz sampling frequency electronic stethoscope. By implementing the noisy segments detection algorithm with this database, a sensitivity of Se=91.41% and a positive predictive value, PPV=92.86% were obtained based on physicians assessments. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Group spike-and-slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information.

    Science.gov (United States)

    Tang, Zaixiang; Shen, Yueping; Li, Yan; Zhang, Xinyan; Wen, Jia; Qian, Chen'ao; Zhuang, Wenzhuo; Shi, Xinghua; Yi, Nengjun

    2018-03-15

    Large-scale molecular data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, standard approaches for omics data analysis ignore the group structure among genes encoded in functional relationships or pathway information. We propose new Bayesian hierarchical generalized linear models, called group spike-and-slab lasso GLMs, for predicting disease outcomes and detecting associated genes by incorporating large-scale molecular data and group structures. The proposed model employs a mixture double-exponential prior for coefficients that induces self-adaptive shrinkage amount on different coefficients. The group information is incorporated into the model by setting group-specific parameters. We have developed a fast and stable deterministic algorithm to fit the proposed hierarchal GLMs, which can perform variable selection within groups. We assess the performance of the proposed method on several simulated scenarios, by varying the overlap among groups, group size, number of non-null groups, and the correlation within group. Compared with existing methods, the proposed method provides not only more accurate estimates of the parameters but also better prediction. We further demonstrate the application of the proposed procedure on three cancer datasets by utilizing pathway structures of genes. Our results show that the proposed method generates powerful models for predicting disease outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). nyi@uab.edu. Supplementary data are available at Bioinformatics online.

  18. Drug facilitated sexual assault: detection and stability of benzodiazepines in spiked drinks using gas chromatography-mass spectrometry.

    Directory of Open Access Journals (Sweden)

    Lata Gautam

    Full Text Available Benzodiazepines are detected in a significant number of drug facilitated sexual assaults (DFSA. Whilst blood and urine from the victim are routinely analysed, due to the delay in reporting DFSA cases and the short half lives of most of these drugs in blood and urine, drug detection in such samples is problematic. Consideration of the drinks involved and analysis for drugs may start to address this. Here we have reconstructed the 'spiking' of three benzodiazepines (diazepam, flunitrazepam and temazepam into five drinks, an alcopop (flavoured alcoholic drink, a beer, a white wine, a spirit, and a fruit based non-alcoholic drink (J2O chosen as representative of those drinks commonly used by women in 16-24 year old age group. Using a validated GC-MS method for the simultaneous detection of these drugs in the drinks we have studied the storage stability of the benzodiazepines under two different storage conditions, uncontrolled room temperature and refrigerator (4°C over a 25 day period. All drugs could be detected in all beverages over this time period. Diazepam was found to be stable in all of the beverages, except the J2O, under both storage conditions. Flunitrazepam and temazepam were found not to be stable but were detectable (97% loss of temazepam and 39% loss of flunitrazepam from J2O. The recommendations from this study are that there should be a policy change and that drinks thought to be involved in DFSA cases should be collected and analysed wherever possible to support other evidence types.

  19. Privacy preserving protocol for detecting genetic relatives using rare variants.

    Science.gov (United States)

    Hormozdiari, Farhad; Joo, Jong Wha J; Wadia, Akshay; Guan, Feng; Ostrosky, Rafail; Sahai, Amit; Eskin, Eleazar

    2014-06-15

    High-throughput sequencing technologies have impacted many areas of genetic research. One such area is the identification of relatives from genetic data. The standard approach for the identification of genetic relatives collects the genomic data of all individuals and stores it in a database. Then, each pair of individuals is compared to detect the set of genetic relatives, and the matched individuals are informed. The main drawback of this approach is the requirement of sharing your genetic data with a trusted third party to perform the relatedness test. In this work, we propose a secure protocol to detect the genetic relatives from sequencing data while not exposing any information about their genomes. We assume that individuals have access to their genome sequences but do not want to share their genomes with anyone else. Unlike previous approaches, our approach uses both common and rare variants which provide the ability to detect much more distant relationships securely. We use a simulated data generated from the 1000 genomes data and illustrate that we can easily detect up to fifth degree cousins which was not possible using the existing methods. We also show in the 1000 genomes data with cryptic relationships that our method can detect these individuals. The software is freely available for download at http://genetics.cs.ucla.edu/crypto/. © The Author 2014. Published by Oxford University Press.

  20. Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW.

    Science.gov (United States)

    Stewart, C M; Newlands, S D; Perachio, A A

    2004-12-01

    Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.

  1. Sensitive seminested PCR method for the detection of Shigella in spiked environmental water samples

    CSIR Research Space (South Africa)

    Theron, J

    2001-03-01

    Full Text Available to shigellae and EIEC and implicated in virulent functions, is present in multiple copies on the invasion plasmid and the chromosome (Venkatesan et al., 1989, 1991; Hartman et al., 1990; Hale, 1991). The standard procedure for Shigella spp. detection is based... on isolation of Shigella by selective culture media followed by identi?cation by biochemical tests and agglutination assays (Frankel et al., 1989; June et al., 1993). This process may take 48?72 h or even longer to obtain results. Since shigellae are very...

  2. Accurate Fall Detection in a Top View Privacy Preserving Configuration.

    Science.gov (United States)

    Ricciuti, Manola; Spinsante, Susanna; Gambi, Ennio

    2018-05-29

    Fall detection is one of the most investigated themes in the research on assistive solutions for aged people. In particular, a false-alarm-free discrimination between falls and non-falls is indispensable, especially to assist elderly people living alone. Current technological solutions designed to monitor several types of activities in indoor environments can guarantee absolute privacy to the people that decide to rely on them. Devices integrating RGB and depth cameras, such as the Microsoft Kinect, can ensure privacy and anonymity, since the depth information is considered to extract only meaningful information from video streams. In this paper, we propose an accurate fall detection method investigating the depth frames of the human body using a single device in a top-view configuration, with the subjects located under the device inside a room. Features extracted from depth frames train a classifier based on a binary support vector machine learning algorithm. The dataset includes 32 falls and 8 activities considered for comparison, for a total of 800 sequences performed by 20 adults. The system showed an accuracy of 98.6% and only one false positive.

  3. Detection and phylogenetic analyses of spike genes in porcine epidemic diarrhea virus strains circulating in China in 2016-2017.

    Science.gov (United States)

    Zhang, Qiaoling; Liu, Xinsheng; Fang, Yuzhen; Zhou, Peng; Wang, Yonglu; Zhang, Yongguang

    2017-10-10

    Large-scale outbreaks of porcine epidemic diarrhea (PED) have re-emerged in China in recent years. However, little is known about the genetic diversity and molecular epidemiology of field strains of PED virus (PEDV) in China in 2016-2017. To address this issue, in this study, 116 diarrhea samples were collected from pig farms in 6 Chinese provinces in 2016-2017 and were detected using PCR for main porcine enteric pathogens, including PEDV, porcine deltacoronavirus (PDCoV), porcine transmissible gastroenteritis virus (TGEV) and porcine kobuvirus (PKV). In addition, the complete S genes from 11 representative PEDV strains were sequenced and analyzed. PCR detection showed that 52.6% (61/116) of these samples were positive for PEDV. Furthermore, sequencing results for the spike (S) genes from 11 of the epidemic PEDV strains showed 93-94% nucleotide identity and 92-93% amino acid identity with the classical CV777 strain. Compared with the CV777 vaccine strain, these strains had an insertion (A 133 ), a deletion (G 155 ), and a continuous 4-amino-acid insertion ( 56 NNTN 59 ) in the S1 region. Phylogenetic analysis based on the S gene indicated that the 11 assessed PEDV strains were genetically diverse and clustered into the G2 group. These results demonstrate that the epidemic strains of PEDV in China in 2016-2017 are mainly virulent strains that belong to the G2 group and genetically differ from the vaccine strain. Importantly, this is the first report that the samples collected in Hainan Province were positive for PEDV (59.2%, 25/42). To our knowledge, this article presents the first report of a virulent PEDV strain isolated from Hainan Island, China. The results of this study will contribute to the understanding of the epidemiology and genetic characteristics of PEDV in China.

  4. Multi-detection of preservatives in cheeses by liquid chromatography-tandem mass spectrometry.

    Science.gov (United States)

    Fuselli, Fabio; Guarino, Chiara; La Mantia, Alessandro; Longo, Lucia; Faberi, Angelo; Marianella, Rosa Maria

    2012-10-01

    The incorrect use of preservatives in cheeses may compromise food safety and damage consumers. According to the law, more than one preservative may be contemporarily used in cheeses. So a method for their contemporary detection may be useful for both manufacturers and control agencies quality control. In this research a liquid chromatography-tandem mass spectrometric with electrospray ionization method for the multi-determination of seven preservatives (benzoic acid, citric acid, hexamethylenetetramine, lysozyme, natamycin, nisin and sorbic acid) in cheese was developed. The preservatives were contemporarily extracted from cheese by a single procedure, and analyzed by RP-LC/ESI-MS/MS (Ion Trap) in positive ionization mode, with single reaction monitoring (SRM) acquisition. Three sample types (hard, pasta filata and fresh cheese) were used for method evaluation. Recoveries were mostly higher than 90%; MDLs ranged from 0.02 to 0.26 mgkg(-1), and MQLs were included between 0.07 and 0.88 mgkg(-1). Due to matrix effect, quantitation was performed by referring to a matrix matched calibration curve, for each cheese typology. This method was also applied to commercial cheese samples, with good results. It appears fast, reliable and suitable for both screening and confirmation of the presence and quantitation of the preservatives in a single, multi-detection analysis. Copyright © 2012 Elsevier B.V. All rights reserved.

  5. Membrane voltage fluctuations reduce spike frequency adaptation and preserve output gain in CA1 pyramidal neurons in a high conductance state

    Science.gov (United States)

    Fernandez, Fernando R.; Broicher, Tilman; Truong, Alan; White, John A.

    2011-01-01

    Modulating the gain of the input-output function of neurons is critical for processing of stimuli and network dynamics. Previous gain control mechanisms have suggested that voltage fluctuations play a key role in determining neuronal gain in vivo. Here we show that, under increased membrane conductance, voltage fluctuations restore Na+ current and reduce spike frequency adaptation in rat hippocampal CA1 pyramidal neurons in vitro. As a consequence, membrane voltage fluctuations produce a leftward shift in the f-I relationship without a change in gain, relative to an increase in conductance alone. Furthermore, we show that these changes have important implications for the integration of inhibitory inputs. Due to the ability to restore Na+ current, hyperpolarizing membrane voltage fluctuations mediated by GABAA-like inputs can increase firing rate in a high conductance state. Finally, our data show that the effects on gain and synaptic integration are mediated by voltage fluctuations within a physiologically relevant range of frequencies (10–40 Hz). PMID:21389243

  6. Wavelet analysis of epileptic spikes

    Science.gov (United States)

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

    2003-05-01

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

  7. 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.

  8. Detection of cervical precancerous lesions with Aptima HPV assays using SurePath preservative fluid specimens

    Directory of Open Access Journals (Sweden)

    Max Chernesky

    2017-06-01

    Full Text Available SurePath specimens from women referred to colposcopy were treated with Aptima Transfer Solution (ATS before testing in Aptima HPV (AHPV and Aptima HPV 16, 18/45 (AHPV-GT assays. Untreated SurePath specimens were tested with the cobas HPV test. PreservCyt specimens were assessed for cytology and tested with AHPV. High-grade cervical intraepithelial neoplasia lesions served as the reference standard. Excellent agreement (95.5%; k=0.91 was observed for ATS-treated SurePath specimens between Tigris and Panther systems and between the PreservCyt and ATS-treated SurePath specimens (91.1%, k=0.81 with the AHPV assay on Tigris. Agreement between the AHPV and cobas assays with SurePath specimens was substantial (89.9%, k=0.80. AHPV sensitivity for CIN2+(n=147 was 91.2% for SurePath and PreservCyt. Cobas HPV sensitivity was 93.9% for SurePath specimens. AHPV testing of SurePath specimens was more specific (59.4% than cobas (54.7% (p<0.001. Detection and genotyping showed similar absolute and relative risks. ATS-treated SurePath specimens tested with AHPV and AHPV-GT assays showed similar performance with greater specificity than cobas HPV on SurePath specimens. Similar overall results were seen using a CIN3 disease endpoint. Keywords: Human papillomavirus, SurePath, PreservCyt, Cervical intraepithelial neoplasia, CIN2+, Aptima transfer solution (ATS

  9. Characterization of novel monoclonal antibodies against the MERS-coronavirus spike protein and their application in species-independent antibody detection by competitive ELISA.

    Science.gov (United States)

    Fukushi, Shuetsu; Fukuma, Aiko; Kurosu, Takeshi; Watanabe, Shumpei; Shimojima, Masayuki; Shirato, Kazuya; Iwata-Yoshikawa, Naoko; Nagata, Noriyo; Ohnishi, Kazuo; Ato, Manabu; Melaku, Simenew Keskes; Sentsui, Hiroshi; Saijo, Masayuki

    2018-01-01

    Since discovering the Middle East respiratory syndrome coronavirus (MERS-CoV) as a causative agent of severe respiratory illness in the Middle East in 2012, serological testing has been conducted to assess antibody responses in patients and to investigate the zoonotic reservoir of the virus. Although the virus neutralization test is the gold standard assay for MERS diagnosis and for investigating the zoonotic reservoir, it uses live virus and so must be performed in high containment laboratories. Competitive ELISA (cELISA), in which a labeled monoclonal antibody (MAb) competes with test serum antibodies for target epitopes, may be a suitable alternative because it detects antibodies in a species-independent manner. In this study, novel MAbs against the spike protein of MERS-CoV were produced and characterized. One of these MAbs was used to develop a cELISA. The cELISA detected MERS-CoV-specific antibodies in sera from MERS-CoV-infected rats and rabbits immunized with the spike protein of MERS-CoV. The MAb-based cELISA was validated using sera from Ethiopian dromedary camels. Relative to the neutralization test, the cELISA detected MERS-CoV-specific antibodies in 66 Ethiopian dromedary camels with a sensitivity and specificity of 98% and 100%, respectively. The cELISA and neutralization test results correlated well (Pearson's correlation coefficients=0.71-0.76, depending on the cELISA serum dilution). This cELISA may be useful for MERS epidemiological investigations on MERS-CoV infection. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. A compressive sensing based secure watermark detection and privacy preserving storage framework.

    Science.gov (United States)

    Qia Wang; Wenjun Zeng; Jun Tian

    2014-03-01

    Privacy is a critical issue when the data owners outsource data storage or processing to a third party computing service, such as the cloud. In this paper, we identify a cloud computing application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We then propose a compressive sensing (CS)-based framework using secure multiparty computation (MPC) protocols to address such a requirement. In our framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a CS domain to protect the privacy. During CS transformation, the privacy of the CS matrix and the watermark pattern is protected by the MPC protocols under the semi-honest security model. We derive the expected watermark detection performance in the CS domain, given the target image, watermark pattern, and the size of the CS matrix (but without the CS matrix itself). The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the CS domain is feasible. Our framework can also be extended to other collaborative secure signal processing and data-mining applications in the cloud.

  11. Evaluation of an automated spike-and-wave complex detection algorithm in the EEG from a rat model of absence epilepsy.

    Science.gov (United States)

    Bauquier, Sebastien H; Lai, Alan; Jiang, Jonathan L; Sui, Yi; Cook, Mark J

    2015-10-01

    The aim of this prospective blinded study was to evaluate an automated algorithm for spike-and-wave discharge (SWD) detection applied to EEGs from genetic absence epilepsy rats from Strasbourg (GAERS). Five GAERS underwent four sessions of 20-min EEG recording. Each EEG was manually analyzed for SWDs longer than one second by two investigators and automatically using an algorithm developed in MATLAB®. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the manual (reference) versus the automatic (test) methods. The results showed that the algorithm had specificity, sensitivity, PPV and NPV >94%, comparable to published methods that are based on analyzing EEG changes in the frequency domain. This provides a good alternative as a method designed to mimic human manual marking in the time domain.

  12. Privacy-Preserved Behavior Analysis and Fall Detection by an Infrared Ceiling Sensor Network

    Directory of Open Access Journals (Sweden)

    Mineichi Kudo

    2012-12-01

    Full Text Available An infrared ceiling sensor network system is reported in this study to realize behavior analysis and fall detection of a single person in the home environment. The sensors output multiple binary sequences from which we know the existence/non-existence of persons under the sensors. The short duration averages of the binary responses are shown to be able to be regarded as pixel values of a top-view camera, but more advantageous in the sense of preserving privacy. Using the “pixel values” as features, support vector machine classifiers succeeded in recognizing eight activities (walking, reading, etc. performed by five subjects at an average recognition rate of 80.65%. In addition, we proposed a martingale framework for detecting falls in this system. The experimental results showed that we attained the best performance of 95.14% (F1 value, the FAR of 7.5% and the FRR of 2.0%. This accuracy is not sufficient in general but surprisingly high with such low-level information. In summary, it is shown that this system has the potential to be used in the home environment to provide personalized services and to detect abnormalities of elders who live alone.

  13. 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

  14. 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.

  15. Feasibility of the development of reference materials for the detection of Ag nanoparticles in food: neat dispersions and spiked chicken meat

    NARCIS (Netherlands)

    Grombe, R.; Allmaier, G.; Charoud-Got, J.; Dudkiewicz, A.; Emteborg, H.; Hofmann, T.; Huusfeldt-Larsen, E.; Lehner, A.; Llinas, M.; Loeschner, K.; Molhave, K.; Peters, R.J.B.; Seghers, J.; Solans, C.; Kammer, van den F.; Wagner, S.; Weigel, S.; Linsinger, T.P.J.

    2015-01-01

    The feasibility of producing colloidal silver nanoparticle reference materials and silver nanoparticle spiked reference matrix materials was investigated. Two concentrations of PVP-coated silver nanoparticle dispersions were evaluated and used to spike chicken meat, with the aim of producing a set

  16. Detect, map, and preserve Bronze & Iron Age monuments along the pre-historic Silk Road

    Science.gov (United States)

    Balz, Timo; Caspari, Gino; Fu, Bihong

    2017-02-01

    Central Asia is rich in cultural heritage generated by thousands of years of human occupation. Aiming for a better understanding of Central Asia’s archaeology and how this unique heritage can be protected, the region should be studied as a whole with regard to its cultural ties with China and combined efforts should be undertaken in shielding the archaeological monuments from destruction. So far, international research campaigns have focused predominantly on single-sites or small-scale surveys, mainly due to the bureaucratic and security related issues involved in cross-border research. This is why we created the Dzungaria Landscape Project. Since 2013, we have worked on collecting remote sensing data of Xinjiang including IKONOS, WorldView-2, and TerraSAR-X data. We have developed a method for the automatic detection of larger grave mound structures in optical and SAR data. Gravemounds are typically spatially clustered and the detection of larger mound structures is a sufficient hint towards areas of high archaeological interest in a region. A meticulous remote sensing survey is the best planning tool for subsequent ground surveys and excavation. In summer 2015, we undertook a survey in the Chinese Altai in order to establish ground-truth in the Hailiutan valley. We categorized over 1000 monuments in just three weeks thanks to the previous detection and classification work using remote sensing data. Creating accurate maps of the cemeteries in northern Xinjiang is a crucial step to preserving the cultural heritage of the region since graves in remote areas are especially prone to looting. We will continue our efforts with the ultimate aim to map and monitor all large gravemounds in Dzungaria and potentially neighbouring eastern Kazakhstan.

  17. Multineuronal Spike Sequences Repeat with Millisecond Precision

    Directory of Open Access Journals (Sweden)

    Koki eMatsumoto

    2013-06-01

    Full Text Available Cortical microcircuits are nonrandomly wired by neurons. As a natural consequence, spikes emitted by microcircuits are also nonrandomly patterned in time and space. One of the prominent spike organizations is a repetition of fixed patterns of spike series across multiple neurons. However, several questions remain unsolved, including how precisely spike sequences repeat, how the sequences are spatially organized, how many neurons participate in sequences, and how different sequences are functionally linked. To address these questions, we monitored spontaneous spikes of hippocampal CA3 neurons ex vivo using a high-speed functional multineuron calcium imaging technique that allowed us to monitor spikes with millisecond resolution and to record the location of spiking and nonspiking neurons. Multineuronal spike sequences were overrepresented in spontaneous activity compared to the statistical chance level. Approximately 75% of neurons participated in at least one sequence during our observation period. The participants were sparsely dispersed and did not show specific spatial organization. The number of sequences relative to the chance level decreased when larger time frames were used to detect sequences. Thus, sequences were precise at the millisecond level. Sequences often shared common spikes with other sequences; parts of sequences were subsequently relayed by following sequences, generating complex chains of multiple sequences.

  18. Depletion of Human DNA in Spiked Clinical Specimens for Improvement of Sensitivity of Pathogen Detection by Next-Generation Sequencing

    OpenAIRE

    Hasan, Mohammad R.; Rawat, Arun; Tang, Patrick; Jithesh, Puthen V.; Thomas, Eva; Tan, Rusung; Tilley, Peter

    2016-01-01

    Next-generation sequencing (NGS) technology has shown promise for the detection of human pathogens from clinical samples. However, one of the major obstacles to the use of NGS in diagnostic microbiology is the low ratio of pathogen DNA to human DNA in most clinical specimens. In this study, we aimed to develop a specimen-processing protocol to remove human DNA and enrich specimens for bacterial and viral DNA for shotgun metagenomic sequencing. Cerebrospinal fluid (CSF) and nasopharyngeal aspi...

  19. Variation in the limit-of-detection of the ProSpecT Campylobacter microplate enzyme immunoassay in stools spiked with emerging Campylobacter species.

    Science.gov (United States)

    Bojanić, Krunoslav; Midwinter, Anne Camilla; Marshall, Jonathan Craig; Rogers, Lynn Elizabeth; Biggs, Patrick Jon; Acke, Els

    2016-08-01

    Campylobacter enteritis in humans is primarily associated with C. jejuni/coli infection. The impact of other Campylobacter spp. is likely to be underestimated due to the bias of culture methods towards Campylobacter jejuni/coli diagnosis. Stool antigen tests are becoming increasingly popular and appear generally less species-specific. A review of independent studies of the ProSpecT® Campylobacter Microplate enzyme immunoassay (EIA) developed for C. jejuni/coli showed comparable diagnostic results to culture methods but the examination of non-jejuni/coli Campylobacter spp. was limited and the limit-of-detection (LOD), where reported, varied between studies. This study investigated LOD of EIA for Campylobacter upsaliensis, Campylobacter hyointestinalis and Campylobacter helveticus spiked in human stools. Multiple stools and Campylobacter isolates were used in three different concentrations (10(4)-10(9)CFU/ml) to reflect sample heterogeneity. All Campylobacter species evaluated were detectable by EIA. Multivariate analysis showed LOD varied between Campylobacter spp. and faecal consistency as fixed effects and individual faecal samples as random effects. EIA showed excellent performance in replicate testing for both within and between batches of reagents, in agreement between visual and spectrophotometric reading of results, and returned no discordance between the bacterial concentrations within independent dilution test runs (positive results with lower but not higher concentrations). This study shows how limitations in experimental procedures lead to an overestimation of consistency and uniformity of LOD for EIA that may not hold under routine use in diagnostic laboratories. Benefits and limitations for clinical practice and the influence on estimates of performance characteristics from detection of multiple Campylobacter spp. by EIA are discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. 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.

  1. Mapping spikes to sensations

    Directory of Open Access Journals (Sweden)

    Maik Christopher Stüttgen

    2011-11-01

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

  2. Spike voltage topography in temporal lobe epilepsy.

    Science.gov (United States)

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

    2016-07-15

    We investigated the voltage topography of interictal spikes in patients with temporal lobe epilepsy (TLE) to see whether topography was related to etiology for TLE. Adults with TLE, who had epilepsy surgery for drug-resistant seizures from 2011 until 2014 at Jefferson Comprehensive Epilepsy Center were selected. Two groups of patients were studied: patients with mesial temporal sclerosis (MTS) on MRI and those with other MRI findings. The voltage topography maps of the interictal spikes at the peak were created using BESA software. We classified the interictal spikes as polar, basal, lateral, or others. Thirty-four patients were studied, from which the characteristics of 340 spikes were investigated. The most common type of spike orientation was others (186 spikes; 54.7%), followed by lateral (146; 42.9%), polar (5; 1.5%), and basal (3; 0.9%). Characteristics of the voltage topography maps of the spikes between the two groups of patients were somewhat different. Five spikes in patients with MTS had polar orientation, but none of the spikes in patients with other MRI findings had polar orientation (odds ratio=6.98, 95% confidence interval=0.38 to 127.38; p=0.07). Scalp topographic mapping of interictal spikes has the potential to offer different information than visual inspection alone. The present results do not allow an immediate clinical application of our findings; however, detecting a polar spike in a patient with TLE may increase the possibility of mesial temporal sclerosis as the underlying etiology. Copyright © 2016 Elsevier B.V. All rights reserved.

  3. Decoding spikes in a spiking neuronal network

    Energy Technology Data Exchange (ETDEWEB)

    Feng Jianfeng [Department of Informatics, University of Sussex, Brighton BN1 9QH (United Kingdom); Ding, Mingzhou [Department of Mathematics, Florida Atlantic University, Boca Raton, FL 33431 (United States)

    2004-06-04

    We investigate how to reliably decode the input information from the output of a spiking neuronal network. A maximum likelihood estimator of the input signal, together with its Fisher information, is rigorously calculated. The advantage of the maximum likelihood estimation over the 'brute-force rate coding' estimate is clearly demonstrated. It is pointed out that the ergodic assumption in neuroscience, i.e. a temporal average is equivalent to an ensemble average, is in general not true. Averaging over an ensemble of neurons usually gives a biased estimate of the input information. A method on how to compensate for the bias is proposed. Reconstruction of dynamical input signals with a group of spiking neurons is extensively studied and our results show that less than a spike is sufficient to accurately decode dynamical inputs.

  4. Decoding spikes in a spiking neuronal network

    International Nuclear Information System (INIS)

    Feng Jianfeng; Ding, Mingzhou

    2004-01-01

    We investigate how to reliably decode the input information from the output of a spiking neuronal network. A maximum likelihood estimator of the input signal, together with its Fisher information, is rigorously calculated. The advantage of the maximum likelihood estimation over the 'brute-force rate coding' estimate is clearly demonstrated. It is pointed out that the ergodic assumption in neuroscience, i.e. a temporal average is equivalent to an ensemble average, is in general not true. Averaging over an ensemble of neurons usually gives a biased estimate of the input information. A method on how to compensate for the bias is proposed. Reconstruction of dynamical input signals with a group of spiking neurons is extensively studied and our results show that less than a spike is sufficient to accurately decode dynamical inputs

  5. Dendritic spikes amplify the synaptic signal to enhance detection of motion in a simulation of the direction-selective ganglion cell.

    Directory of Open Access Journals (Sweden)

    Michael J Schachter

    2010-08-01

    Full Text Available The On-Off direction-selective ganglion cell (DSGC in mammalian retinas responds most strongly to a stimulus moving in a specific direction. The DSGC initiates spikes in its dendritic tree, which are thought to propagate to the soma with high probability. Both dendritic and somatic spikes in the DSGC display strong directional tuning, whereas somatic PSPs (postsynaptic potentials are only weakly directional, indicating that spike generation includes marked enhancement of the directional signal. We used a realistic computational model based on anatomical and physiological measurements to determine the source of the enhancement. Our results indicate that the DSGC dendritic tree is partitioned into separate electrotonic regions, each summing its local excitatory and inhibitory synaptic inputs to initiate spikes. Within each local region the local spike threshold nonlinearly amplifies the preferred response over the null response on the basis of PSP amplitude. Using inhibitory conductances previously measured in DSGCs, the simulation results showed that inhibition is only sufficient to prevent spike initiation and cannot affect spike propagation. Therefore, inhibition will only act locally within the dendritic arbor. We identified the role of three mechanisms that generate directional selectivity (DS in the local dendritic regions. First, a mechanism for DS intrinsic to the dendritic structure of the DSGC enhances DS on the null side of the cell's dendritic tree and weakens it on the preferred side. Second, spatially offset postsynaptic inhibition generates robust DS in the isolated dendritic tips but weak DS near the soma. Third, presynaptic DS is apparently necessary because it is more robust across the dendritic tree. The pre- and postsynaptic mechanisms together can overcome the local intrinsic DS. These local dendritic mechanisms can perform independent nonlinear computations to make a decision, and there could be analogous mechanisms within

  6. Dendritic spikes amplify the synaptic signal to enhance detection of motion in a simulation of the direction-selective ganglion cell.

    Science.gov (United States)

    Schachter, Michael J; Oesch, Nicholas; Smith, Robert G; Taylor, W Rowland

    2010-08-19

    The On-Off direction-selective ganglion cell (DSGC) in mammalian retinas responds most strongly to a stimulus moving in a specific direction. The DSGC initiates spikes in its dendritic tree, which are thought to propagate to the soma with high probability. Both dendritic and somatic spikes in the DSGC display strong directional tuning, whereas somatic PSPs (postsynaptic potentials) are only weakly directional, indicating that spike generation includes marked enhancement of the directional signal. We used a realistic computational model based on anatomical and physiological measurements to determine the source of the enhancement. Our results indicate that the DSGC dendritic tree is partitioned into separate electrotonic regions, each summing its local excitatory and inhibitory synaptic inputs to initiate spikes. Within each local region the local spike threshold nonlinearly amplifies the preferred response over the null response on the basis of PSP amplitude. Using inhibitory conductances previously measured in DSGCs, the simulation results showed that inhibition is only sufficient to prevent spike initiation and cannot affect spike propagation. Therefore, inhibition will only act locally within the dendritic arbor. We identified the role of three mechanisms that generate directional selectivity (DS) in the local dendritic regions. First, a mechanism for DS intrinsic to the dendritic structure of the DSGC enhances DS on the null side of the cell's dendritic tree and weakens it on the preferred side. Second, spatially offset postsynaptic inhibition generates robust DS in the isolated dendritic tips but weak DS near the soma. Third, presynaptic DS is apparently necessary because it is more robust across the dendritic tree. The pre- and postsynaptic mechanisms together can overcome the local intrinsic DS. These local dendritic mechanisms can perform independent nonlinear computations to make a decision, and there could be analogous mechanisms within cortical circuitry.

  7. Preserving information in neural transmission.

    Science.gov (United States)

    Sincich, Lawrence C; Horton, Jonathan C; Sharpee, Tatyana O

    2009-05-13

    Along most neural pathways, the spike trains transmitted from one neuron to the next are altered. In the process, neurons can either achieve a more efficient stimulus representation, or extract some biologically important stimulus parameter, or succeed at both. We recorded the inputs from single retinal ganglion cells and the outputs from connected lateral geniculate neurons in the macaque to examine how visual signals are relayed from retina to cortex. We found that geniculate neurons re-encoded multiple temporal stimulus features to yield output spikes that carried more information about stimuli than was available in each input spike. The coding transformation of some relay neurons occurred with no decrement in information rate, despite output spike rates that averaged half the input spike rates. This preservation of transmitted information was achieved by the short-term summation of inputs that geniculate neurons require to spike. A reduced model of the retinal and geniculate visual responses, based on two stimulus features and their associated nonlinearities, could account for >85% of the total information available in the spike trains and the preserved information transmission. These results apply to neurons operating on a single time-varying input, suggesting that synaptic temporal integration can alter the temporal receptive field properties to create a more efficient representation of visual signals in the thalamus than the retina.

  8. 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.

  9. Cardiac Involvement in Myotonic Dystrophy Type 2 Patients With Preserved Ejection Fraction: Detection by Cardiovascular Magnetic Resonance.

    Science.gov (United States)

    Schmacht, Luisa; Traber, Julius; Grieben, Ulrike; Utz, Wolfgang; Dieringer, Matthias A; Kellman, Peter; Blaszczyk, Edyta; von Knobelsdorff-Brenkenhoff, Florian; Spuler, Simone; Schulz-Menger, Jeanette

    2016-07-01

    Myotonic dystrophy type 2 (DM2) is a genetic disorder characterized by skeletal muscle symptoms, metabolic changes, and cardiac involvement. Histopathologic alterations of the skeletal muscle include fibrosis and fatty infiltration. The aim of this study was to investigate whether subclinical cardiac involvement in DM2 is already detectable in preserved left ventricular function by cardiovascular magnetic resonance. Twenty-seven patients (mean age, 54±10 years; 20 females) with a genetically confirmed diagnosis of DM2 were compared with 17 healthy age- and sex-matched controls using a 1.5 T magnetic resonance imaging. For myocardial tissue differentiation, T1 and T2 mapping, fat/water-separated imaging, focal fibrosis imaging (late gadolinium enhancement [LGE]), and (1)H magnetic resonance spectroscopy were performed. Extracellular volume fraction was calculated. Conduction abnormalities were diagnosed based on Groh criteria. LGE located subepicardial basal inferolateral was detectable in 22% of the patients. Extracellular volume was increased in this region and in the adjacent medial inferolateral segment (P=0.03 compared with healthy controls). In 21% of patients with DM2, fat deposits were detectable (all women). The control group showed no abnormalities. Myocardial triglycerides were not different in LGE-positive and LGE-negative subjects (P=0.47). Six patients had indicators for conduction disease (60% of LGE-positive patients and 12.5% of LGE-negative patients). In DM2, subclinical myocardial injury was already detectable in preserved left ventricular ejection fraction. Extracellular volume was also increased in regions with no focal fibrosis. Myocardial fibrosis was related to conduction abnormalities. © 2016 American Heart Association, Inc.

  10. A Novel and Simple Spike Sorting Implementation.

    Science.gov (United States)

    Petrantonakis, Panagiotis C; Poirazi, Panayiota

    2017-04-01

    Monitoring the activity of multiple, individual neurons that fire spikes in the vicinity of an electrode, namely perform a Spike Sorting (SS) procedure, comprises one of the most important tools for contemporary neuroscience in order to reverse-engineer the brain. As recording electrodes' technology rabidly evolves by integrating thousands of electrodes in a confined spatial setting, the algorithms that are used to monitor individual neurons from recorded signals have to become even more reliable and computationally efficient. In this work, we propose a novel framework of the SS approach in which a single-step processing of the raw (unfiltered) extracellular signal is sufficient for both the detection and sorting of the activity of individual neurons. Despite its simplicity, the proposed approach exhibits comparable performance with state-of-the-art approaches, especially for spike detection in noisy signals, and paves the way for a new family of SS algorithms with the potential for multi-recording, fast, on-chip implementations.

  11. Brightness-preserving fuzzy contrast enhancement scheme for the detection and classification of diabetic retinopathy disease.

    Science.gov (United States)

    Datta, Niladri Sekhar; Dutta, Himadri Sekhar; Majumder, Koushik

    2016-01-01

    The contrast enhancement of retinal image plays a vital role for the detection of microaneurysms (MAs), which are an early sign of diabetic retinopathy disease. A retinal image contrast enhancement method has been presented to improve the MA detection technique. The success rate on low-contrast noisy retinal image analysis shows the importance of the proposed method. Overall, 587 retinal input images are tested for performance analysis. The average sensitivity and specificity are obtained as 95.94% and 99.21%, respectively. The area under curve is found as 0.932 for the receiver operating characteristics analysis. The classifications of diabetic retinopathy disease are also performed here. The experimental results show that the overall MA detection method performs better than the current state-of-the-art MA detection algorithms.

  12. 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)

  13. Feasibility of the development of reference materials for the detection of Ag nanoparticles in food: neat dispersions and spiked chicken meat

    DEFF Research Database (Denmark)

    Grombe, Ringo; Allmaier, Günter; Charoud-Got, Jean

    2015-01-01

    as reference materials. Stability studies at 4 °C, 18 °C and 60 °C demonstrated sufficient short- and long-term stability, although particle size decreases in a linear fashion at 60 °C. The AgNP dispersions were characterized for total Ag mass fraction by ICP-OES, dissolved Ag content by ultrafiltration...... in homogeneous and stable materials. The spiked chicken materials were characterized for their total Ag mass fraction by neutron activation analysis and for the AgNP particle size by TEM and single-particle inductively coupled plasma mass spectrometry. The observed differences in particle sizes between...

  14. Chlamydia trachomatis detection in cervical PreservCyt specimens from an Irish urban female population.

    LENUS (Irish Health Repository)

    Keegan, H

    2012-02-01

    OBJECTIVE: The aim of this study was to determine the prevalence of cervical Chlamydia trachomatis infection by polymerase chain reaction (PCR) in urban women undergoing routine cervical cytological screening and to investigate the relationship with age, cytology, smoking status and concurrent human papillomavirus (HPV) infection. METHODS: A total of 996 women (age range 16-69 years) attending general practitioners for routine liquid-based cervical smear screening in the Dublin area were recruited in the study of prevalence of C. trachomatis. Informed consent was obtained and liquid-based cytology (LBC) specimens were sent for cytological screening. DNA was extracted from residual LBC and tested for C. trachomatis by PCR using the highly sensitive C. trachomatis plasmid (CTP) primers and for HPV infection using the MY09\\/11 primers directed to the HPV L1 gene in a multiplex format. RESULTS: The overall prevalence of C. trachomatis was 5.4%. Prevalence was highest in the <25 years age group (10%). Coinfection with HPV and C. trachomatis occurred in 1% of the screening population. A higher rate of smoking was observed in women positive for C. trachomatis, HPV infections or those with abnormal cervical cytology. Chlamydia trachomatis infection was not associated with abnormal cytology. CONCLUSIONS: Women (5.4%) presenting for routine cervical screening are infected with C. trachomatis. Opportunistic screening for C. trachomatis from PreservCyt sample taken at the time of cervical cytological screening may be a possible strategy to screen for C. trachomatis in the Irish female population.

  15. Tolerance Induction of Temperature and Starvation with Tricalcium Phosphate on Preservation and Sporulation in Bacillus amyloliquefaciens Detected by Flow Cytometry.

    Science.gov (United States)

    Shahrokh Esfahani, Samaneh; Emtiazi, Giti; Shafiei, Rasoul; Ghorbani, Najmeh; Zarkesh Esfahani, Seyed Hamid

    2016-09-01

    The Bacillus species have many applications in the preparation of various enzymes, probiotic, biofertilizer, and biomarkers for which the survival of resting cells and spore formation under different conditions are important. In this study, water and saline along with different mineral substances such as calcium carbonate, calcium phosphate, and silica were used for the detection of survival and preservation of Bacillus amyloliquefaciens. The results showed intensive death of resting cells at 8 °C, but significant survival at 28 °C after one month. However, preservation by minerals significantly decreased the rate of death and induced sporulation at both the temperatures. The resting cells were maintained at room temperature (about 60 % of the initial population survived after a month) in the presence of tricalcium phosphate. The results showed that temperature has more effect on sporulation compare with starvation. The sporulation in normal saline at 28 °C was 70 times more than that at 8 °C; meanwhile, addition of tricalcium phosphate increases sporulation by 90 times. Also, the FTIR data showed the interaction of tricalcium phosphate with spores and resting cells. The discrimination of sporulation from non-sporulation state was performed by nucleic acid staining with thiazole orange and detected by flow cytometry. The flow cytometric studies confirmed that the rates of sporulation in pure water were significantly more at 28 °C. This is the first report on the detection of bacterial spore with thiazole orange by flow cytometry and also on the interaction of tricalcium phosphate with spores by FTIR analyses.

  16. A Privacy-Preserving Framework for Collaborative Intrusion Detection Networks Through Fog Computing

    DEFF Research Database (Denmark)

    Wang, Yu; Xie, Lin; Li, Wenjuan

    2017-01-01

    Nowadays, cyber threats (e.g., intrusions) are distributed across various networks with the dispersed networking resources. Intrusion detection systems (IDSs) have already become an essential solution to defend against a large amount of attacks. With the development of cloud computing, a modern IDS...

  17. The EPR detection of foods preserved with the use of ionizing radiation

    Science.gov (United States)

    Stachowicz, W.; Burlińska, G.; Michalik, J.; Dziedzic-Gocławska, A.; Ostrowski, K.

    1995-02-01

    Solid constituents extracted from irradiated foods have been examined by the epr (esr) spectroscopy. It has been proved that some epr active species produced by radiation in foods are specific and stable enough to be used for the detection of irradiation treatment. The most promising results have been obtained with bones extracted from frozen raw meat (beef, pork, poultry and fish), with seeds of fruits (dates and figs), with dried mushrooms, gelatin and macaroni.

  18. 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

  19. Privacy-Preserving Detection of Inter-Domain SDN Rules Overlaps

    KAUST Repository

    Dethise, Arnaud

    2017-08-24

    SDN approaches to inter-domain routing promise better traffic engineering, enhanced security, and higher automation. Yet, naïve deployment of SDN on the Internet is dangerous as the control-plane expressiveness of BGP is significantly more limited than the data-plane expressiveness of SDN, which allows fine-grained rules to deflect traffic from BGP\\'s default routes. This mismatch may lead to incorrect forwarding behaviors such as forwarding loops and blackholes, ultimately hindering SDN deployment at the inter-domain level. In this work, we make a first step towards verifying the correctness of inter-domain forwarding state with a focus on loop freedom while keeping private the SDN rules, as they comprise confidential routing information. To this end, we design a simple yet powerful primitive that allows two networks to verify whether their SDN rules overlap, i.e., the set of packets matched by these rules is non-empty, without leaking any information about the SDN rules. We propose an efficient implementation of this primitive by using recent advancements in Secure Multi-Party Computation and we then leverage it as the main building block for designing a system that detects Internet-wide forwarding loops among any set of SDN-enabled Internet eXchange Points.

  20. Reduced detection by Ziehl-Neelsen method of acid-fast bacilli in sputum samples preserved in cetylpyridinium chloride solution.

    Science.gov (United States)

    Selvakumar, N; Sudhamathi, S; Duraipandian, M; Frieden, T R; Narayanan, P R

    2004-02-01

    Twelve health facilities implementing the DOTS strategy, and the Tuberculosis Research Centre (TRC), Chennai, India. To determine the detection rates using Ziehl-Neelsen (ZN) and auramine-phenol to stain acid-fast bacilli (AFB) in sputum samples stored in cetylpyridinium chloride (CPC) solution. Two smears were prepared from each of 988 sputum samples collected in CPC and randomly allocated, one to ZN and the other to auramine-phenol staining. All samples were processed for culture of Mycobacterium tuberculosis. A significantly higher proportion of samples were negative using the ZN method compared to the auramine-phenol method (74.5% vs. 61.8%, McNamara's paired chi2 test; P < 0.001). Among 377 samples that were positive using auramine-phenol, 44% were negative using ZN. There were more culture-positive, smear-negative samples in ZN (52.7%) than in auramine-phenol (30%); the difference attained statistical significance (McNemar's paired chi2 test; P < 0.00004). Using ZN, of the 104 smears made immediately after collection, 52 were positive for AFB, of which only 35 (67.3%) were positive after storage in CPC; the reduction in the number of positive smears attained statistical significance (McNemar's paired chi2 test; P = 0.004). Detection of AFB in sputum samples preserved in CPC is significantly reduced using ZN staining.

  1. 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.

  2. SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

    Science.gov (United States)

    Zenke, Friedemann; Ganguli, Surya

    2018-04-13

    A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

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

    Science.gov (United States)

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

    2013-01-01

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

  4. Unsupervised spike sorting based on discriminative subspace learning.

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2014-01-01

    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

  5. Detection of Cryptosporidium sp infection by PCR and modified acid fast staining from potassium dichromate preserved stool

    Directory of Open Access Journals (Sweden)

    Agnes Kurniawan

    2009-09-01

    Full Text Available Aim To identify the frequency of Cryptosporidium infection in children below 3 years old by examining concentrated long term preserved stool using PCR detection of 18S rRNA gene and compared with modified acid fast staining technique.Methods Hundred eighty eight stools from children ≤ 3 years old were stored for 13 months in 2.5% K2Cr2O7 solution at 40C. Cryptosporidium oocysts were isolated by water-ether concentration technique. The concentrates were smeared onto object glass and stained with modified acid fast staining, and the rest of the concentrates were DNA extracted by freezing and thawing cycles and proteinase K digestion, then direct PCR was done to detect 18S rRNA gene.Result The proportion of positive stools for Cryptosporidium sp by acid fast staining from concentrated stools and 18S rRNA PCR were 4.8% and 34.6% respectively, which showed statistically significant difference.Conclusion The frequency of Cryptosporidium infection among children ≤ 3 years old was very high and stool storage in K2Cr2O7 for 13 months did not affect the PCR result. High prevalence of Cryptosporidium infection indicated high transmission in that area and the potential to be transmitted to other individuals such as the immunocompromised. (Med J Indones 2009;18:147-52Key words: 18S rRNA, cryptosporidiosis

  6. Development of monitoring protocols to detect change in rocky intertidal communities of Glacier Bay National Park and Preserve

    Science.gov (United States)

    Irvine, Gail V.

    2010-01-01

    Glacier Bay National Park and Preserve in southeastern Alaska includes extensive coastlines representing a major proportion of all coastlines held by the National Park Service. The marine plants and invertebrates that occupy intertidal shores form highly productive communities that are ecologically important to a number of vertebrate and invertebrate consumers and that are vulnerable to human disturbances. To better understand these communities and their sensitivity, it is important to obtain information on species abundances over space and time. During field studies from 1997 to 2001, I investigated probability-based rocky intertidal monitoring designs that allow inference of results to similar habitat within the bay and that reduce bias. Aerial surveys of a subset of intertidal habitat indicated that the original target habitat of bedrock-dominated sites with slope less than or equal to 30 degrees was rare. This finding illustrated the value of probability-based surveys and led to a shift in the target habitat type to more mixed rocky habitat with steeper slopes. Subsequently, I investigated different sampling methods and strategies for their relative power to detect changes in the abundances of the predominant sessile intertidal taxa: barnacles -Balanomorpha, the mussel Mytilus trossulus and the rockweed Fucus distichus subsp. evanescens. I found that lower-intensity sampling of 25 randomly selected sites (= coarse-grained sampling) provided a greater ability to detect changes in the abundances of these taxa than did more intensive sampling of 6 sites (= fine-grained sampling). Because of its greater power, the coarse-grained sampling scheme was adopted in subsequent years. This report provides detailed analyses of the 4 years of data and evaluates the relative effect of different sampling attributes and management-set parameters on the ability of the sampling to detect changes in the abundances of these taxa. The intent was to provide managers with information

  7. 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

  8. Wood preservation

    Science.gov (United States)

    Kevin Archer; Stan Lebow

    2006-01-01

    Wood preservation can be interpreted to mean protection from fire, chemical degradation, mechanical wear, weathering, as well as biological attack. In this chapter, the term preservation is applied more restrictively to protection from biological hazards.

  9. A novel unsupervised spike sorting algorithm for intracranial EEG.

    Science.gov (United States)

    Yadav, R; Shah, A K; Loeb, J A; Swamy, M N S; Agarwal, R

    2011-01-01

    This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.

  10. The transfer function of neuron spike.

    Science.gov (United States)

    Palmieri, Igor; Monteiro, Luiz H A; Miranda, Maria D

    2015-08-01

    The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in the surroundings of the nerve cell. The model is presented as a cascade association of two subsystems: one that generates an action potential from an input stimulus, and one that represents the medium between the cell and the electrode. The suggested approach employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical processes of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. The model is validated by using in vivo experimental readings of intracellular and extracellular signals. A computational simulation of the model is presented in order to assess its proximity to the neuronal signal and to observe the variability of the estimated parameters. The implications of the results are discussed in the context of spike sorting. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Comparison of Classifier Architectures for Online Neural Spike Sorting.

    Science.gov (United States)

    Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

    2017-04-01

    High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.

  12. [A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

    Science.gov (United States)

    Zhang, Y; Liu, A; Yu, K

    1999-06-01

    A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

  13. Software preservation

    Directory of Open Access Journals (Sweden)

    Tadej Vodopivec

    2011-01-01

    Full Text Available Comtrade Ltd. covers a wide range of activities related to information and communication technologies; its deliverables include web applications, locally installed programs,system software, drivers, embedded software (used e.g. in medical devices, auto parts,communication switchboards. Also the extensive knowledge and practical experience about digital long-term preservation technologies have been acquired. This wide spectrum of activities puts us in the position to discuss the often overlooked aspect of the digital preservation - preservation of software programs. There are many resources dedicated to digital preservation of digital data, documents and multimedia records,but not so many about how to preserve the functionalities and features of computer programs. Exactly these functionalities - dynamic response to inputs - render the computer programs rich compared to documents or linear multimedia. The article opens the questions on the beginning of the way to the permanent digital preservation. The purpose is to find a way in the right direction, where all relevant aspects will be covered in proper balance. The following questions are asked: why at all to preserve computer programs permanently, who should do this and for whom, when we should think about permanent program preservation, what should be persevered (such as source code, screenshots, documentation, and social context of the program - e.g. media response to it ..., where and how? To illustrate the theoretic concepts given the idea of virtual national museum of electronic banking is also presented.

  14. Detection of Cryptosporidium sp infection by PCR and modified acid fast staining from potassium dichromate preserved stool

    OpenAIRE

    Agnes Kurniawan; Sri W. Dwintasari; Herbowo A. Soetomenggolo; Septelia I. Wanandi

    2009-01-01

    Aim To identify the frequency of Cryptosporidium infection in children below 3 years old by examining concentrated long term preserved stool using PCR detection of 18S rRNA gene and compared with modified acid fast staining technique.Methods Hundred eighty eight stools from children ≤ 3 years old were stored for 13 months in 2.5% K2Cr2O7 solution at 40C. Cryptosporidium oocysts were isolated by water-ether concentration technique. The concentrates were smeared onto object glass and stained wi...

  15. An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

    Science.gov (United States)

    Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

    2015-01-01

    A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction. PMID:26287193

  16. An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm.

    Science.gov (United States)

    Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

    2015-08-13

    A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

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

    OpenAIRE

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

    2013-01-01

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

  18. Comparison of spike-sorting algorithms for future hardware implementation.

    Science.gov (United States)

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

    2008-01-01

    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 transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.

  19. Computing with Spiking Neuron Networks

    NARCIS (Netherlands)

    H. Paugam-Moisy; S.M. Bohte (Sander); G. Rozenberg; T.H.W. Baeck (Thomas); J.N. Kok (Joost)

    2012-01-01

    htmlabstractAbstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd gener- ation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an ac- curate modeling of synaptic interactions

  20. Learning Universal Computations with Spikes

    Science.gov (United States)

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

    2016-01-01

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

  1. Automatic spike sorting using tuning information.

    Science.gov (United States)

    Ventura, Valérie

    2009-09-01

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

  2. Digital preservation

    CERN Document Server

    Deegan, Marilyn

    2013-01-01

    Digital preservation is an issue of huge importance to the library and information profession right now. With the widescale adoption of the internet and the rise of the world wide web, the world has been overwhelmed by digital information. Digital data is being produced on a massive scale by individuals and institutions: some of it is born, lives and dies only in digital form, and it is the potential death of this data, with its impact on the preservation of culture, that is the concern of this book. So how can information professionals try to remedy this? Digital preservation is a complex iss

  3. Independent component analysis separates spikes of different origin in the EEG.

    Science.gov (United States)

    Urrestarazu, Elena; Iriarte, Jorge; Artieda, Julio; Alegre, Manuel; Valencia, Miguel; Viteri, César

    2006-02-01

    Independent component analysis (ICA) is a novel system that finds independent sources in recorded signals. Its usefulness in separating epileptiform activity of different origin has not been determined. The goal of this study was to demonstrate that ICA is useful for separating different spikes using samples of EEG of patients with focal epilepsy. Digital EEG samples from four patients with focal epilepsy were included. The patients had temporal (n = 2), centrotemporal (n = 1) or frontal spikes (n = 1). Twenty-six samples with two (or more) spikes from two different patients were created. The selection of the two spikes for each mixed EEG was performed randomly, trying to have all the different combinations and rejecting the mixture of two spikes from the same patient. Two different examiners studied the EEGs using ICA with JADE paradigm in Matlab platform, trying to separate and to identify the spikes. They agreed in the correct separation of the spikes in 24 of the 26 samples, classifying the spikes as frontal, temporal or centrotemporal, left or right sided. The demonstration of the possibility of detecting different artificially mixed spikes confirms that ICA may be useful in separating spikes or other elements in real EEGs.

  4. Urine Preservative

    Science.gov (United States)

    Smith, Scott M. (Inventor); Nillen, Jeannie (Inventor)

    2001-01-01

    Disclosed is CPG, a combination of a chlorhexidine salt (such as chlorhexidine digluconate, chlorhexidine diacetate, or chlorhexidine dichloride) and n-propyl gallate that can be used at ambient temperatures as a urine preservative.

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

    Science.gov (United States)

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

    2016-01-01

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

  6. 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

  7. 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.

  8. 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)

  9. 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

  10. Asymptomatic loss of intraepidermal nerve fibers with preserved thermal detection thresholds after repeated exposure to severe cold

    DEFF Research Database (Denmark)

    Krøigård, Thomas; Wirenfeldt, Martin; Svendsen, Toke K.

    2018-01-01

    Background: Cold-induced peripheral neuropathy has been described in individuals exposed to severe cold resulting in pain, hypersensitivity to cold, hyperhidrosis, numbness, and skin changes. Nerve conduction studies and thermal detection thresholds are abnormal in symptomatic patients......-induced peripheral neuropathy may be prevalent in subjects living in or near polar regions which could have implications for the recruitment of healthy subjects....

  11. Spikes Filtering with Neural Networks: a Two-Stage Detection System Filtrage des pics par des réseaux neuronaux : un système de détection à deux étages

    Directory of Open Access Journals (Sweden)

    Mousset E.

    2006-11-01

    Full Text Available A two-stage system for detecting spikes in seismic data has been developed, each stage using neural networks (NN techniques. The first stage is trained and used on a running preprocessing window over traces ; its goal is to satisfy the three following criteria (by decreasing priority :(a Maximize the number of detections. (b Minimize the CPU-cost. (c Minimize the number of false alarms. The second stage processes the first stage's alarms in order to discriminate between true and false ones. Several preprocessing techniques, and especially their discriminatory power (to separate noise and signal were tested :(a Based on energy criteria. (b Based on frequency spectrum. (c Based on signal attributes, as Hilbert attributes, or other signal features. Several NN architectures, with global, local and constrained connections were compared. NN behavior at neighborhood of decision area was observed in order to determine a selection method of relevant decision thresholds. The first stage was tested on raw traces issued from 250 shots of a real twodimensional onshore seismic campaign. Three different migrated sections (Dip Moveout were compared. The first was obtained by applying on the latter raw traces a conventional processing sequence including an equalization phase, the second by omitting the equalization phase and the third by both including a prior NN filtering of raw traces and omitting the equalization phase. Afin de détecter les spikes au sein des traces sismiques brutes, nous avons développé un système composé de deux étages, chacun d'eux faisant intervenir un réseau de neurones artificiels dans ses calculs. Le premier réseau est entraîné pour traiter chaque trace au moyen d'une fenêtre glissante et doit satisfaire les trois critères suivants (par ordre décroissant de priorité : - maximiser le nombre de détections; - minimiser la consommation CPU; - minimiser le nombre de fausses alarmes. Le second étage est entraîné à partir

  12. 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

  13. Emittance preservation

    Energy Technology Data Exchange (ETDEWEB)

    Kain, V; Arduini, G; Goddard, B; Holzer, B J; Jowett, J M; Meddahi, M; Mertens, T; Roncarolo, F; Schaumann, M; Versteegen, R; Wenninger, J [European Organization for Nuclear Research, Geneva (Switzerland)

    2012-07-01

    Emittance measurements during the LHC proton run 2011 indicated a blow-up of 20 % to 30 % from LHC injection to collisions. This presentation will show the emittance preservation throughout the different parts of the LHC cycle and discuss the current limitations on emittance determination. An overview of emittance preservation through the injector complex as function of bunch intensity will also be given. Possible sources for the observed blow-up and required tests in 2012 will be presented. Possible improvements of emittance diagnostics and analysis tools for 2012 will be shown.

  14. Is it clean or contaminated soil? Using petrogenic versus biogenic GC-FID chromatogram patterns to mathematically resolve false petroleum hydrocarbon detections in clean organic soils: a crude oil-spiked peat microcosm experiment.

    Science.gov (United States)

    Kelly-Hooper, Francine; Farwell, Andrea J; Pike, Glenna; Kennedy, Jocelyn; Wang, Zhendi; Grunsky, Eric C; Dixon, D George

    2013-10-01

    The Canadian Council of Ministers of the Environment (CCME) reference method for the Canada-wide standard (CWS) for petroleum hydrocarbon (PHC) in soil provides chemistry analysis standards and guidelines for the management of contaminated sites. However, these methods can coextract natural biogenic organic compounds (BOCs) from organic soils, causing false exceedences of toxicity guidelines. The present 300-d microcosm experiment used CWS PHC tier 1 soil extraction and gas chromatography-flame ionization detector (GC-FID) analysis to develop a new tier 2 mathematical approach to resolving this problem. Carbon fractions F2 (C10-C16), F3 (C16-C34), and F4 (>C34) as well as subfractions F3a (C16-C22) and F3b (C22-C34) were studied in peat and sand spiked once with Federated crude oil. These carbon ranges were also studied in 14 light to heavy crude oils. The F3 range in the clean peat was dominated by F3b, whereas the crude oils had approximately equal F3a and F3b distributions. The F2 was nondetectable in the clean peat but was a significant component in crude oil. The crude oil–spiked peat had elevated F2 and F3a distributions. The BOC-adjusted PHC F3 calculation estimated the true PHC concentrations in the spiked peat. The F2:F3b ratio of less than 0.10 indicated PHC absence in the clean peat, and the ratio of greater than or equal to 0.10 indicated PHC presence in the spiked peat and sand. Validation studies are required to confirm whether this new tier 2 approach is applicable to real-case scenarios. Potential adoption of this approach could minimize unnecessary ecological disruptions of thousands of peatlands throughout Canada while also saving millions of dollars in management costs. © 2013 SETAC.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-03-01

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

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

    International Nuclear Information System (INIS)

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

    2013-01-01

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

  17. Sialic Acid Binding Properties of Soluble Coronavirus Spike (S1 Proteins: Differences between Infectious Bronchitis Virus and Transmissible Gastroenteritis Virus

    Directory of Open Access Journals (Sweden)

    Christine Winter

    2013-07-01

    Full Text Available The spike proteins of a number of coronaviruses are able to bind to sialic acids present on the cell surface. The importance of this sialic acid binding ability during infection is, however, quite different. We compared the spike protein of transmissible gastroenteritis virus (TGEV and the spike protein of infectious bronchitis virus (IBV. Whereas sialic acid is the only receptor determinant known so far for IBV, TGEV requires interaction with its receptor aminopeptidase N to initiate infection of cells. Binding tests with soluble spike proteins carrying an IgG Fc-tag revealed pronounced differences between these two viral proteins. Binding of the IBV spike protein to host cells was in all experiments sialic acid dependent, whereas the soluble TGEV spike showed binding to APN but had no detectable sialic acid binding activity. Our results underline the different ways in which binding to sialoglycoconjugates is mediated by coronavirus spike proteins.

  18. A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'.

    Science.gov (United States)

    Swindale, Nicholas V; Mitelut, Catalin; Murphy, Timothy H; Spacek, Martin A

    2017-02-10

    Few stand-alone software applications are available for sorting spikes from recordings made with multi-electrode arrays. Ideally, an application should be user friendly with a graphical user interface, able to read data files in a variety of formats, and provide users with a flexible set of tools giving them the ability to detect and sort extracellular voltage waveforms from different units with some degree of reliability. Previously published spike sorting methods are now available in a software program, SpikeSorter, intended to provide electrophysiologists with a complete set of tools for sorting, starting from raw recorded data file and ending with the export of sorted spikes times. Procedures are automated to the extent this is currently possible. The article explains and illustrates the use of the program. A representative data file is opened, extracellular traces are filtered, events are detected and then clustered. A number of problems that commonly occur during sorting are illustrated, including the artefactual over-splitting of units due to the tendency of some units to fire spikes in pairs where the second spike is significantly smaller than the first, and over-splitting caused by slow variation in spike height over time encountered in some units. The accuracy of SpikeSorter's performance has been tested with surrogate ground truth data and found to be comparable to that of other algorithms in current development.

  19. 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

  20. Motor control by precisely timed spike patterns

    DEFF Research Database (Denmark)

    Srivastava, Kyle H; Holmes, Caroline M; Vellema, Michiel

    2017-01-01

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

  1. High Throughput Sequencing to Detect Differences in Methanotrophic Methylococcaceae and Methylocystaceae in Surface Peat, Forest Soil, and Sphagnum Moss in Cranesville Swamp Preserve, West Virginia, USA

    Science.gov (United States)

    Lau, Evan; Nolan, Edward J.; Dillard, Zachary W.; Dague, Ryan D.; Semple, Amanda L.; Wentzell, Wendi L.

    2015-01-01

    Northern temperate forest soils and Sphagnum-dominated peatlands are a major source and sink of methane. In these ecosystems, methane is mainly oxidized by aerobic methanotrophic bacteria, which are typically found in aerated forest soils, surface peat, and Sphagnum moss. We contrasted methanotrophic bacterial diversity and abundances from the (i) organic horizon of forest soil; (ii) surface peat; and (iii) submerged Sphagnum moss from Cranesville Swamp Preserve, West Virginia, using multiplex sequencing of bacterial 16S rRNA (V3 region) gene amplicons. From ~1 million reads, >50,000 unique OTUs (Operational Taxonomic Units), 29 and 34 unique sequences were detected in the Methylococcaceae and Methylocystaceae, respectively, and 24 potential methanotrophs in the Beijerinckiaceae were also identified. Methylacidiphilum-like methanotrophs were not detected. Proteobacterial methanotrophic bacteria constitute Sphagnum moss) or co-occurred in both Sphagnum moss and peat. This study provides insights into the structure of methanotrophic communities in relationship to habitat type, and suggests that peat and Sphagnum moss can influence methanotroph community structure and biogeography. PMID:27682082

  2. Voltage spikes in Nb3Sn and NbTi strands

    International Nuclear Information System (INIS)

    Bordini, B.; Ambrosio, G.; Barzi, E.; Carcagno, R.; Feher, S.; Kashikhin, V.V.; Lamm, M.J.; Orris, D.; Tartaglia, M.; Tompkins, J.C.; Turrioni, D.; Yamada, R.; Zlobin, A.V.; Fermilab

    2005-01-01

    As part of the High Field Magnet program at Fermilab several NbTi and Nb 3 Sn strands were tested with particular emphasis on the study of voltage spikes and their relationship to superconductor instabilities. The voltage spikes were detected under various experimental conditions using voltage-current (V-I) and voltage-field (V-H) methods. Two types of spikes, designated ''magnetization'' and ''transport current'' spikes, have been identified. Their origin is most likely related to magnetization flux jump and transport current redistribution, respectively. Many of the signals observed appear to be a combination of these two types of spikes; the combination of these two instability mechanisms should play a dominant role in determining the minimum quench current

  3. Voltage spikes in Nb3Sn and NbTi strands

    Energy Technology Data Exchange (ETDEWEB)

    Bordini, B.; Ambrosio, G.; Barzi, E.; Carcagno, R.; Feher, S.; Kashikhin, V.V.; Lamm, M.J.; Orris, D.; Tartaglia, M.; Tompkins, J.C.; Turrioni, D.; Yamada, R.; Zlobin,; /Fermilab

    2005-09-01

    As part of the High Field Magnet program at Fermilab several NbTi and Nb{sub 3}Sn strands were tested with particular emphasis on the study of voltage spikes and their relationship to superconductor instabilities. The voltage spikes were detected under various experimental conditions using voltage-current (V-I) and voltage-field (V-H) methods. Two types of spikes, designated ''magnetization'' and ''transport current'' spikes, have been identified. Their origin is most likely related to magnetization flux jump and transport current redistribution, respectively. Many of the signals observed appear to be a combination of these two types of spikes; the combination of these two instability mechanisms should play a dominant role in determining the minimum quench current.

  4. Robust spike sorting of retinal ganglion cells tuned to spot stimuli.

    Science.gov (United States)

    Ghahari, Alireza; Badea, Tudor C

    2016-08-01

    We propose an automatic spike sorting approach for the data recorded from a microelectrode array during visual stimulation of wild type retinas with tiled spot stimuli. The approach first detects individual spikes per electrode by their signature local minima. With the mixture probability distribution of the local minima estimated afterwards, it applies a minimum-squared-error clustering algorithm to sort the spikes into different clusters. A template waveform for each cluster per electrode is defined, and a number of reliability tests are performed on it and its corresponding spikes. Finally, a divisive hierarchical clustering algorithm is used to deal with the correlated templates per cluster type across all the electrodes. According to the measures of performance of the spike sorting approach, it is robust even in the cases of recordings with low signal-to-noise ratio.

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

    Science.gov (United States)

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

    2013-03-01

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

  6. 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

    and moving visual stimuli from the spontaneous ongoing spiking state, in all layers and zones of areas 17 and 18 indicating that the second threshold is a property of the network. Spontaneous and evoked spiking, thus can easily be distinguished. In addition, the trajectories of spontaneous ongoing states......Most neurons have a threshold separating the silent non-spiking state and the state of producing temporal sequences of spikes. But neurons in vivo also have a second threshold, found recently in granular layer neurons of the primary visual cortex, separating spontaneous ongoing spiking from...... visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary...

  7. 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)

  8. Bayesian population decoding of spiking neurons.

    Science.gov (United States)

    Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

    2009-01-01

    The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

  9. Bayesian population decoding of spiking neurons

    Directory of Open Access Journals (Sweden)

    Sebastian Gerwinn

    2009-10-01

    Full Text Available The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a `spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

  10. Statistical properties of superimposed stationary spike trains.

    Science.gov (United States)

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

    2012-06-01

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

  11. 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

  12. Mimickers of generalized spike and wave discharges.

    Science.gov (United States)

    Azzam, Raed; Bhatt, Amar B

    2014-06-01

    Overinterpretation of benign EEG variants is a common problem that can lead to the misdiagnosis of epilepsy. We review four normal patterns that mimic generalized spike and wave discharges: phantom spike-and-wave, hyperventilation hypersynchrony, hypnagogic/ hypnopompic hypersynchrony, and mitten patterns.

  13. 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.

  14. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Stochastic variational learning in recurrent spiking networks.

    Science.gov (United States)

    Jimenez Rezende, Danilo; Gerstner, Wulfram

    2014-01-01

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

  16. Decoding spatiotemporal spike sequences via the finite state automata dynamics of spiking neural networks

    International Nuclear Information System (INIS)

    Jin, Dezhe Z

    2008-01-01

    Temporally complex stimuli are encoded into spatiotemporal spike sequences of neurons in many sensory areas. Here, we describe how downstream neurons with dendritic bistable plateau potentials can be connected to decode such spike sequences. Driven by feedforward inputs from the sensory neurons and controlled by feedforward inhibition and lateral excitation, the neurons transit between UP and DOWN states of the membrane potentials. The neurons spike only in the UP states. A decoding neuron spikes at the end of an input to signal the recognition of specific spike sequences. The transition dynamics is equivalent to that of a finite state automaton. A connection rule for the networks guarantees that any finite state automaton can be mapped into the transition dynamics, demonstrating the equivalence in computational power between the networks and finite state automata. The decoding mechanism is capable of recognizing an arbitrary number of spatiotemporal spike sequences, and is insensitive to the variations of the spike timings in the sequences

  17. Reliability of MEG source imaging of anterior temporal spikes: analysis of an intracranially characterized spike focus.

    Science.gov (United States)

    Wennberg, Richard; Cheyne, Douglas

    2014-05-01

    To assess the reliability of MEG source imaging (MSI) of anterior temporal spikes through detailed analysis of the localization and orientation of source solutions obtained for a large number of spikes that were separately confirmed by intracranial EEG to be focally generated within a single, well-characterized spike focus. MSI was performed on 64 identical right anterior temporal spikes from an anterolateral temporal neocortical spike focus. The effects of different volume conductors (sphere and realistic head model), removal of noise with low frequency filters (LFFs) and averaging multiple spikes were assessed in terms of the reliability of the source solutions. MSI of single spikes resulted in scattered dipole source solutions that showed reasonable reliability for localization at the lobar level, but only for solutions with a goodness-of-fit exceeding 80% using a LFF of 3 Hz. Reliability at a finer level of intralobar localization was limited. Spike averaging significantly improved the reliability of source solutions and averaging 8 or more spikes reduced dependency on goodness-of-fit and data filtering. MSI performed on topographically identical individual spikes from an intracranially defined classical anterior temporal lobe spike focus was limited by low reliability (i.e., scattered source solutions) in terms of fine, sublobar localization within the ipsilateral temporal lobe. Spike averaging significantly improved reliability. MSI performed on individual anterior temporal spikes is limited by low reliability. Reduction of background noise through spike averaging significantly improves the reliability of MSI solutions. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  18. Improving small RNA-seq by using a synthetic spike-in set for size-range quality control together with a set for data normalization.

    Science.gov (United States)

    Locati, Mauro D; Terpstra, Inez; de Leeuw, Wim C; Kuzak, Mateusz; Rauwerda, Han; Ensink, Wim A; van Leeuwen, Selina; Nehrdich, Ulrike; Spaink, Herman P; Jonker, Martijs J; Breit, Timo M; Dekker, Rob J

    2015-08-18

    There is an increasing interest in complementing RNA-seq experiments with small-RNA (sRNA) expression data to obtain a comprehensive view of a transcriptome. Currently, two main experimental challenges concerning sRNA-seq exist: how to check the size distribution of isolated sRNAs, given the sensitive size-selection steps in the protocol; and how to normalize data between samples, given the low complexity of sRNA types. We here present two separate sets of synthetic RNA spike-ins for monitoring size-selection and for performing data normalization in sRNA-seq. The size-range quality control (SRQC) spike-in set, consisting of 11 oligoribonucleotides (10-70 nucleotides), was tested by intentionally altering the size-selection protocol and verified via several comparative experiments. We demonstrate that the SRQC set is useful to reproducibly track down biases in the size-selection in sRNA-seq. The external reference for data-normalization (ERDN) spike-in set, consisting of 19 oligoribonucleotides, was developed for sample-to-sample normalization in differential-expression analysis of sRNA-seq data. Testing and applying the ERDN set showed that it can reproducibly detect differential expression over a dynamic range of 2(18). Hence, biological variation in sRNA composition and content between samples is preserved while technical variation is effectively minimized. Together, both spike-in sets can significantly improve the technical reproducibility of sRNA-seq. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  19. Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

    DEFF Research Database (Denmark)

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

    2016-01-01

    attractor. Its existence guarantees that evoked spiking return to the spontaneous state. However, the spontaneous ongoing spiking state and the visual evoked spiking states are qualitatively different and are separated by a threshold (separatrix). The functional advantage of this organization...

  20. 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.

  1. Method for spiking soil samples with organic compounds

    DEFF Research Database (Denmark)

    Brinch, Ulla C; Ekelund, Flemming; Jacobsen, Carsten S

    2002-01-01

    We examined the harmful side effects on indigenous soil microorganisms of two organic solvents, acetone and dichloromethane, that are normally used for spiking of soil with polycyclic aromatic hydrocarbons for experimental purposes. The solvents were applied in two contamination protocols to either...... higher than in control soil, probably due mainly to release of predation from indigenous protozoa. In order to minimize solvent effects on indigenous soil microorganisms when spiking native soil samples with compounds having a low water solubility, we propose a common protocol in which the contaminant...... tagged with luxAB::Tn5. For both solvents, application to the whole sample resulted in severe side effects on both indigenous protozoa and bacteria. Application of dichloromethane to the whole soil volume immediately reduced the number of protozoa to below the detection limit. In one of the soils...

  2. Analysis of voltage spikes in superconducting Nb3Sn magnets

    International Nuclear Information System (INIS)

    Rahimzadeh-Kalaleh, S.; Ambrosio, G.; Chlachidze, G.; Donnelly, C.

    2008-01-01

    Fermi National Accelerator Laboratory has been developing a new generation of superconducting accelerator magnets based on Niobium Tin (Nb 3 Sn). The performance of these magnets is influenced by thermo-magnetic instabilities, known as flux jumps, which can lead to premature trips of the quench detection system due to large voltage transients or quenches at low current. In an effort to better characterize and understand these instabilities, a system for capturing fast voltage transients was developed and used in recent tests of R and D model magnets. A new automated voltage spike analysis program was developed for the analysis of large amount of voltage-spike data. We report results from the analysis of large statistics data samples for short model magnets that were constructed using MJR and RRP strands having different sub-element size and structure. We then assess the implications for quench protection of Nb 3 Sn magnets

  3. 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.

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

    Science.gov (United States)

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

    2016-01-01

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

  5. Mixed signal learning by spike correlation propagation in feedback inhibitory circuits.

    Directory of Open Access Journals (Sweden)

    Naoki Hiratani

    2015-04-01

    Full Text Available The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory.

  6. Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study

    International Nuclear Information System (INIS)

    Shimazaki, Hideaki

    2013-01-01

    Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model) with a state-space method (Shimazaki et al. 2012 PLoS Comput Biol 8 e1002385). This method allows us to estimate stimulus-evoked dynamics of neuronal interactions which is reproducible in repeated trials under identical experimental conditions. However, the method may not be suitable for detecting stimulus responses if the neuronal dynamics exhibits significant variability across trials. In addition, the previous model does not include effects of past spiking activity of the neurons on the current state of ensemble activity. In this study, we develop a parametric method for simultaneously estimating the stimulus and spike-history effects on the ensemble activity from single-trial data even if the neurons exhibit dynamics that is largely unrelated to these effects. For this goal, we model ensemble neuronal activity as a latent process and include the stimulus and spike-history effects as exogenous inputs to the latent process. We develop an expectation-maximization algorithm that simultaneously achieves estimation of the latent process, stimulus responses, and spike-history effects. The proposed method is useful to analyze an interaction of internal cortical states and sensory evoked activity

  7. 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

  8. 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

  9. Spike persistence and normalization in benign epilepsy with centrotemporal spikes - Implications for management.

    Science.gov (United States)

    Kim, Hunmin; Kim, Soo Yeon; Lim, Byung Chan; Hwang, Hee; Chae, Jong-Hee; Choi, Jieun; Kim, Ki Joong; Dlugos, Dennis J

    2018-05-10

    This study was performed 1) to determine the timing of spike normalization in patients with benign epilepsy with centrotemporal spikes (BECTS); 2) to identify relationships between age of seizure onset, age of spike normalization, years of spike persistence and treatment; and 3) to assess final outcomes between groups of patients with or without spikes at the time of medication tapering. Retrospective analysis of BECTS patients confirmed by clinical data, including age of onset, seizure semiology and serial electroencephalography (EEG) from diagnosis to remission. Age at spike normalization, years of spike persistence, and time of treatment onset to spike normalization were assessed. Final seizure and EEG outcome were compared between the groups with or without spikes at the time of AED tapering. One hundred and thirty-four patients were included. Mean age at seizure onset was 7.52 ± 2.11 years. Mean age at spike normalization was 11.89 ± 2.11 (range: 6.3-16.8) years. Mean time of treatment onset to spike normalization was 4.11 ± 2.13 (range: 0.24-10.08) years. Younger age of seizure onset was correlated with longer duration of spike persistence (r = -0.41, p < 0.001). In treated patients, spikes persisted for 4.1 ± 1.95 years, compared with 2.9 ± 1.97 years in untreated patients. No patients had recurrent seizures after AED was discontinued, regardless of the presence/absence of spikes at time of AED tapering. Years of spike persistence was longer in early onset BECTS patients. Treatment with AEDs did not shorten years of spike persistence. Persistence of spikes at time of treatment withdrawal was not associated with seizure recurrence. Copyright © 2018 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

  10. Memristors Empower Spiking Neurons With Stochasticity

    KAUST Repository

    Al-Shedivat, Maruan; Naous, Rawan; Cauwenberghs, Gert; Salama, Khaled N.

    2015-01-01

    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

  11. Fitting neuron models to spike trains

    Directory of Open Access Journals (Sweden)

    Cyrille eRossant

    2011-02-01

    Full Text Available Computational modeling is increasingly used to understand the function of neural circuitsin systems neuroscience.These studies require models of individual neurons with realisticinput-output properties.Recently, it was found that spiking models can accurately predict theprecisely timed spike trains produced by cortical neurons in response tosomatically injected currents,if properly fitted. This requires fitting techniques that are efficientand flexible enough to easily test different candidate models.We present a generic solution, based on the Brian simulator(a neural network simulator in Python, which allowsthe user to define and fit arbitrary neuron models to electrophysiological recordings.It relies on vectorization and parallel computing techniques toachieve efficiency.We demonstrate its use on neural recordings in the barrel cortex andin the auditory brainstem, and confirm that simple adaptive spiking modelscan accurately predict the response of cortical neurons. Finally, we show how a complexmulticompartmental model can be reduced to a simple effective spiking model.

  12. Frequency of Rolandic Spikes in ADHD

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2003-10-01

    Full Text Available The frequency of rolandic spikes in nonepileptic children with attention deficit hyperactivity disorder (ADHD was compared with a control group of normal school-aged children in a study at the University of Frankfurt, Germany.

  13. THE POLITICAL CRITIQUE OF SPIKE Lee's Bamboozled

    African Journals Online (AJOL)

    Admin

    CONTEMPORARY AMERICAN MEDIA: THE POLITICAL. CRITIQUE OF SPIKE ... KEYWORDS: Blackface Minstrelsy, Racist Stereotypes and American Media. INTRODUCTION ..... of a difference that is itself a process of disavowal.” In this ...

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

    Science.gov (United States)

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

    2013-12-01

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

  15. Anticipating Activity in Social Media Spikes

    OpenAIRE

    Higham, Desmond J.; Grindrod, Peter; Mantzaris, Alexander V.; Otley, Amanda; Laflin, Peter

    2014-01-01

    We propose a novel mathematical model for the activity of microbloggers during an external, event-driven spike. The model leads to a testable prediction of who would become most active if a spike were to take place. This type of information is of great interest to commercial organisations, governments and charities, as it identifies key players who can be targeted with information in real time when the network is most receptive. The model takes account of the fact that dynamic interactions ev...

  16. Building functional networks of spiking model neurons.

    Science.gov (United States)

    Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin

    2016-03-01

    Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

  17. [Analysis of the preservative chlorphenesin in cosmetics by high performance liquid chromatography].

    Science.gov (United States)

    Zhu, Huijuan; Zhang, Weiqiang; Yang, Yanwei; Zhu, Ying

    2014-01-01

    An analytical method was developed for the determination of the preservative of chlorphenesin in cosmetics by high performance liquid chromatography (HPLC). A C18 column (250 mm x 4.6 mm, 5 microm) and a photodiode array detector were used. The mobile phase was methanol-water (55:45, v/v) with a flow rate of 1.0 mL/min. The detection wavelength was set at 280 nm and the column temperature was 25 degrees C. The limit of detection was 3 ng. A good linear relationship was obtained between the peak area and the mass concentration of chlorphenesin in the range of 1 - 500 mg/L and the correlation coefficient was 1.000 0. The recoveries of chlorphenesin at different spiked levels were 99.0% - 103% with the relative standard deviations (RSD) chlorphenesin in cosmetics.

  18. 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.

  19. 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

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

  20. 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.

  1. 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.

  2. A Low Cost VLSI Architecture for Spike Sorting Based on Feature Extraction with Peak Search

    Directory of Open Access Journals (Sweden)

    Yuan-Jyun Chang

    2016-12-01

    Full Text Available The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO. The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.

  3. A Low Cost VLSI Architecture for Spike Sorting Based on Feature Extraction with Peak Search.

    Science.gov (United States)

    Chang, Yuan-Jyun; Hwang, Wen-Jyi; Chen, Chih-Chang

    2016-12-07

    The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO). The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.

  4. Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

    Science.gov (United States)

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

    2016-01-01

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

  5. Non-orthogonally transitive G2 spike solution

    International Nuclear Information System (INIS)

    Lim, Woei Chet

    2015-01-01

    We generalize the orthogonally transitive (OT) G 2 spike solution to the non-OT G 2 case. This is achieved by applying Geroch’s transformation on a Kasner seed. The new solution contains two more parameters than the OT G 2 spike solution. Unlike the OT G 2 spike solution, the new solution always resolves its spike. (fast track communication)

  6. An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

    Directory of Open Access Journals (Sweden)

    Ying-Lun Chen

    2015-08-01

    Full Text Available A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO, and the feature extraction is carried out by the generalized Hebbian algorithm (GHA. To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

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

    Science.gov (United States)

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

    2016-01-01

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

  8. Spike rate and spike timing contributions to coding taste quality information in rat periphery

    Directory of Open Access Journals (Sweden)

    Vernon eLawhern

    2011-05-01

    Full Text Available There is emerging evidence that individual sensory neurons in the rodent brain rely on temporal features of the discharge pattern to code differences in taste quality information. In contrast, in-vestigations of individual sensory neurons in the periphery have focused on analysis of spike rate and mostly disregarded spike timing as a taste quality coding mechanism. The purpose of this work was to determine the contribution of spike timing to taste quality coding by rat geniculate ganglion neurons using computational methods that have been applied successfully in other sys-tems. We recorded the discharge patterns of narrowly-tuned and broadly-tuned neurons in the rat geniculate ganglion to representatives of the five basic taste qualities. We used mutual in-formation to determine significant responses and the van Rossum metric to characterize their temporal features. While our findings show that spike timing contributes a significant part of the message, spike rate contributes the largest portion of the message relayed by afferent neurons from rat fungiform taste buds to the brain. Thus, spike rate and spike timing together are more effective than spike rate alone in coding stimulus quality information to a single basic taste in the periphery for both narrowly-tuned specialist and broadly-tuned generalist neurons.

  9. Application of unfolding transformation in the random matrix theory to analyze in vivo neuronal spike firing during awake and anesthetized conditions

    Directory of Open Access Journals (Sweden)

    Risako Kato

    2018-03-01

    Full Text Available General anesthetics decrease the frequency and density of spike firing. This effect makes it difficult to detect spike regularity. To overcome this problem, we developed a method utilizing the unfolding transformation which analyzes the energy level statistics in the random matrix theory. We regarded the energy axis as time axis of neuron spike and analyzed the time series of cortical neural firing in vivo. Unfolding transformation detected regularities of neural firing while changes in firing densities were associated with pentobarbital. We found that unfolding transformation enables us to compare firing regularity between awake and anesthetic conditions on a universal scale. Keywords: Unfolding transformation, Spike-timing, Regularity

  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. Spike Code Flow in Cultured Neuronal Networks.

    Science.gov (United States)

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

    2016-01-01

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

  12. Spike Code Flow in Cultured Neuronal Networks

    Directory of Open Access Journals (Sweden)

    Shinichi Tamura

    2016-01-01

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

  13. The electric potential of tripolar spikes

    Energy Technology Data Exchange (ETDEWEB)

    Nocera, L. [Theoretical Plasma Physics, IPCF-CNR, Via Moruzzi 1, I-56124 Pisa (Italy)

    2010-02-22

    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.

  14. 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.

  15. Trace element ink spiking for signature authentication

    International Nuclear Information System (INIS)

    Hatzistavros, V.S.; Kallithrakas-Kontos, N.G.

    2008-01-01

    Signature authentication is a critical question in forensic document examination. Last years the evolution of personal computers made signature copying a quite easy task, so the development of new ways for signature authentication is crucial. In the present work a commercial ink was spiked with many trace elements in various concentrations. Inorganic and organometallic ink soluble compounds were used as spiking agents, whilst ink retained its initial properties. The spiked inks were used for paper writing and the documents were analyzed by a non destructive method, the energy dispersive X-ray fluorescence. The thin target model was proved right for quantitative analysis and a very good linear relationship of the intensity (X-ray signal) against concentration was estimated for all used elements. Intensity ratios between different elements in the same ink gave very stable results, independent on the writing alterations. The impact of time both to written document and prepared inks was also investigated. (author)

  16. Spike timing precision of neuronal circuits.

    Science.gov (United States)

    Kilinc, Deniz; Demir, Alper

    2018-04-17

    Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.

  17. Galactic center gamma-ray excess from dark matter annihilation: is there a black hole spike?

    Science.gov (United States)

    Fields, Brian D; Shapiro, Stuart L; Shelton, Jessie

    2014-10-10

    If the supermassive black hole Sgr A* at the center of the Milky Way grew adiabatically from an initial seed embedded in a Navarro-Frenk-White dark matter (DM) halo, then the DM profile near the hole has steepened into a spike. We calculate the dramatic enhancement to the gamma-ray flux from the Galactic center (GC) from such a spike if the 1-3 GeV excess observed in Fermi data is due to DM annihilations. We find that for the parameter values favored in recent fits, the point-source-like flux from the spike is 35 times greater than the flux from the inner 1° of the halo, far exceeding all Fermi point source detections near the GC. We consider the dependence of the spike signal on astrophysical and particle parameters and conclude that if the GC excess is due to DM, then a canonical adiabatic spike is disfavored by the data. We discuss alternative Galactic histories that predict different spike signals, including (i) the nonadiabatic growth of the black hole, possibly associated with halo and/or black hole mergers, (ii) gravitational interaction of DM with baryons in the dense core, such as heating by stars, or (iii) DM self-interactions. We emphasize that the spike signal is sensitive to a different combination of particle parameters than the halo signal and that the inclusion of a spike component to any DM signal in future analyses would provide novel information about both the history of the GC and the particle physics of DM annihilations.

  18. Evoking prescribed spike times in stochastic neurons

    Science.gov (United States)

    Doose, Jens; Lindner, Benjamin

    2017-09-01

    Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.

  19. Temporal Correlations and Neural Spike Train Entropy

    International Nuclear Information System (INIS)

    Schultz, Simon R.; Panzeri, Stefano

    2001-01-01

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

  20. 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

  1. 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.

  2. 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.

  3. 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?

  4. An Unsupervised Online Spike-Sorting Framework.

    Science.gov (United States)

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

    2016-08-01

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

  5. 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.

  6. Spatiotemporal mapping of interictal spike propagation: a novel methodology applied to pediatric intracranial EEG recordings.

    Directory of Open Access Journals (Sweden)

    Samuel Tomlinson

    2016-12-01

    Full Text Available Synchronized cortical activity is implicated in both normative cognitive functioning andmany neurological disorders. For epilepsy patients with intractable seizures, irregular patterns ofsynchronization within the epileptogenic zone (EZ is believed to provide the network substratethrough which seizures initiate and propagate. Mapping the EZ prior to epilepsy surgery is critical fordetecting seizure networks in order to achieve post-surgical seizure control. However, automatedtechniques for characterizing epileptic networks have yet to gain traction in the clinical setting.Recent advances in signal processing and spike detection have made it possible to examine thespatiotemporal propagation of interictal spike discharges across the epileptic cortex. In this study, wepresent a novel methodology for detecting, extracting, and visualizing spike propagation anddemonstrate its potential utility as a biomarker for the epileptogenic zone. Eighteen pre-surgicalintracranial EEG recordings were obtained from pediatric patients ultimately experiencing favorable(i.e., seizure-free, n = 9 or unfavorable (i.e., seizure-persistent, n = 9 surgical outcomes. Novelalgorithms were applied to extract multi-channel spike discharges and visualize their spatiotemporalpropagation. Quantitative analysis of spike propagation was performed using trajectory clusteringand spatial autocorrelation techniques. Comparison of interictal propagation patterns revealed anincrease in trajectory organization (i.e., spatial autocorrelation among Sz-Free patients compared toSz-Persist patients. The pathophysiological basis and clinical implications of these findings areconsidered.

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

    Science.gov (United States)

    Russo, Giulia; Spinella, Salvatore; Sciacca, Eva; Bonfante, Paola; Genre, Andrea

    2013-12-26

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

  8. 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.

  9. 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

  10. 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.

  11. Spike-timing dependent plasticity in the striatum

    Directory of Open Access Journals (Sweden)

    Elodie Fino

    2010-06-01

    Full Text Available The striatum is the major input nucleus of basal ganglia, an ensemble of interconnected sub-cortical nuclei associated with fundamental processes of action-selection and procedural learning and memory. The striatum receives afferents from the cerebral cortex and the thalamus. In turn, it relays the integrated information towards the basal ganglia output nuclei through which it operates a selected activation of behavioral effectors. The striatal output neurons, the GABAergic medium-sized spiny neurons (MSNs, are in charge of the detection and integration of behaviorally relevant information. This property confers to the striatum the ability to extract relevant information from the background noise and select cognitive-motor sequences adapted to environmental stimuli. As long-term synaptic efficacy changes are believed to underlie learning and memory, the corticostriatal long-term plasticity provides a fundamental mechanism for the function of the basal ganglia in procedural learning. Here, we reviewed the different forms of spike-timing dependent plasticity (STDP occurring at corticostriatal synapses. Most of the studies have focused on MSNs and their ability to develop long-term plasticity. Nevertheless, the striatal interneurons (the fast-spiking GABAergic, the NO synthase and cholinergic interneurons also receive monosynaptic afferents from the cortex and tightly regulated corticostriatal information processing. Therefore, it is important to take into account the variety of striatal neurons to fully understand the ability of striatum to develop long-term plasticity. Corticostriatal STDP with various spike-timing dependence have been observed depending on the neuronal sub-populations and experimental conditions. This complexity highlights the extraordinary potentiality in term of plasticity of the corticostriatal pathway.

  12. Spike timing rigidity is maintained in bursting neurons under pentobarbital-induced anesthetic conditions

    Directory of Open Access Journals (Sweden)

    Risako Kato

    2016-11-01

    Full Text Available Pentobarbital potentiates γ-aminobutyric acid (GABA-mediated inhibitory synaptic transmission by prolonging the open time of GABAA receptors. However, it is unknown how pentobarbital regulates cortical neuronal activities via local circuits in vivo. To examine this question, we performed extracellular unit recording in rat insular cortex under awake and anesthetic conditions. Not a few studies apply time-rescaling theorem to detect the features of repetitive spike firing. Similar to these methods, we define an average spike interval locally in time using random matrix theory (RMT, which enables us to compare different activity states on a universal scale. Neurons with high spontaneous firing frequency (> 5 Hz and bursting were classified as HFB neurons (n = 10, and those with low spontaneous firing frequency (< 10 Hz and without bursting were classified as non-HFB neurons (n = 48. Pentobarbital injection (30 mg/kg reduced firing frequency in all HFB neurons and in 78% of non-HFB neurons. RMT analysis demonstrated that pentobarbital increased in the number of neurons with repulsion in both HFB and non-HFB neurons, suggesting that there is a correlation between spikes within a short interspike interval. Under awake conditions, in 50% of HFB and 40% of non-HFB neurons, the decay phase of normalized histograms of spontaneous firing were fitted to an exponential function, which indicated that the first spike had no correlation with subsequent spikes. In contrast, under pentobarbital-induced anesthesia conditions, the number of non-HFB neurons that were fitted to an exponential function increased to 80%, but almost no change in HFB neurons was observed. These results suggest that under both awake and pentobarbital-induced anesthetized conditions, spike firing in HFB neurons is more robustly regulated by preceding spikes than by non-HFB neurons, which may reflect the GABAA receptor-mediated regulation of cortical activities. Whole-cell patch

  13. 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...

  14. 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.

  15. 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...

  16. Error-backpropagation in temporally encoded networks of spiking neurons

    NARCIS (Netherlands)

    S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)

    2000-01-01

    textabstractFor a network of spiking neurons that encodes information in the timing of individual spike-times, we derive a supervised learning rule, emph{SpikeProp, akin to traditional error-backpropagation and show how to overcome the discontinuities introduced by thresholding. With this algorithm,

  17. Immunohistochemical detection of PGL-1, LAM, 30 kD and 65 kD antigens in leprosy infected paraffin preserved skin and nerve sections

    NARCIS (Netherlands)

    van den Bos, I. C.; Khanolkar-Young, S.; Das, P. K.; Lockwood, D. N.

    1999-01-01

    A panel of lipid, carbohydrate and protein antibodies were optimized for use in detecting M. leprae antigens in paraffin embedded material. Skin and nerve biopsies from 13 patients across the leprosy spectrum were studied. All antibodies detected antigen in tissues with a BI > 1. Phenolic-glycolipid

  18. Hardware implementation of stochastic spiking neural networks.

    Science.gov (United States)

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

    2012-08-01

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

  19. Evolving spiking networks with variable resistive memories.

    Science.gov (United States)

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

    2014-01-01

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

  20. Visualizing spikes in source-space

    DEFF Research Database (Denmark)

    Beniczky, Sándor; Duez, Lene; Scherg, Michael

    2016-01-01

    OBJECTIVE: Reviewing magnetoencephalography (MEG) recordings is time-consuming: signals from the 306 MEG-sensors are typically reviewed divided into six arrays of 51 sensors each, thus browsing each recording six times in order to evaluate all signals. A novel method of reconstructing the MEG...... signals in source-space was developed using a source-montage of 29 brain-regions and two spatial components to remove magnetocardiographic (MKG) artefacts. Our objective was to evaluate the accuracy of reviewing MEG in source-space. METHODS: In 60 consecutive patients with epilepsy, we prospectively...... evaluated the accuracy of reviewing the MEG signals in source-space as compared to the classical method of reviewing them in sensor-space. RESULTS: All 46 spike-clusters identified in sensor-space were also identified in source-space. Two additional spike-clusters were identified in source-space. As 29...

  1. Increasing rock-avalanche size and mobility in Glacier Bay National Park and Preserve, Alaska detected from 1984 to 2016 Landsat imagery

    Science.gov (United States)

    Coe, Jeffrey A.; Bessette-Kirton, Erin; Geertsema, Marten

    2018-01-01

    In the USA, climate change is expected to have an adverse impact on slope stability in Alaska. However, to date, there has been limited work done in Alaska to assess if changes in slope stability are occurring. To address this issue, we used 30-m Landsat imagery acquired from 1984 to 2016 to establish an inventory of 24 rock avalanches in a 5000-km2 area of Glacier Bay National Park and Preserve in southeast Alaska. A search of available earthquake catalogs revealed that none of the avalanches were triggered by earthquakes. Analyses of rock-avalanche magnitude, mobility, and frequency reveal a cluster of large (areas ranging from 5.5 to 22.2 km2), highly mobile (height/length slopes for failure during periods of warm temperatures.

  2. 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.

  3. Stochastic synchronization in finite size spiking networks

    Science.gov (United States)

    Doiron, Brent; Rinzel, John; Reyes, Alex

    2006-09-01

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

  4. 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)

  5. A Fully Automated Approach to Spike Sorting.

    Science.gov (United States)

    Chung, Jason E; Magland, Jeremy F; Barnett, Alex H; Tolosa, Vanessa M; Tooker, Angela C; Lee, Kye Y; Shah, Kedar G; Felix, Sarah H; Frank, Loren M; Greengard, Leslie F

    2017-09-13

    Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. 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.

  7. Ripples on spikes show increased phase-amplitude coupling in mesial temporal lobe epilepsy seizure onset zones

    Science.gov (United States)

    Weiss, Shennan A; Orosz, Iren; Salamon, Noriko; Moy, Stephanie; Wei, Linqing; Van ’t Klooster, Maryse A; Knight, Robert T; Harper, Ronald M; Bragin, Anatol; Fried, Itzhak; Engel, Jerome; Staba, Richard J

    2016-01-01

    Objective Ripples (80–150 Hz) recorded from clinical macroelectrodes have been shown to be an accurate biomarker of epileptogenic brain tissue. We investigated coupling between epileptiform spike phase and ripple amplitude to better understand the mechanisms that generate this type of pathological ripple (pRipple) event. Methods We quantified phase amplitude coupling (PAC) between epileptiform EEG spike phase and ripple amplitude recorded from intracranial depth macroelectrodes during episodes of sleep in 12 patients with mesial temporal lobe epilepsy. PAC was determined by 1) a phasor transform that corresponds to the strength and rate of ripples coupled with spikes, and a 2) ripple-triggered average to measure the strength, morphology, and spectral frequency of the modulating and modulated signals. Coupling strength was evaluated in relation to recording sites within and outside the seizure onset zone (SOZ). Results Both the phasor transform and ripple-triggered averaging methods showed ripple amplitude was often robustly coupled with epileptiform EEG spike phase. Coupling was more regularly found inside than outside the SOZ, and coupling strength correlated with the likelihood a macroelectrode’s location was within the SOZ (pripples coupled with EEG spikes inside the SOZ to rates of coupled ripples in non-SOZ was greater than the ratio of rates of ripples on spikes detected irrespective of coupling (pripple amplitude (pripple spectral frequency (pripple amplitude. The changes in excitability reflected as epileptiform spikes may also cause clusters of pathologically interconnected bursting neurons to grow and synchronize into aberrantly large neuronal assemblies. PMID:27723936

  8. 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.

  9. 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.

  10. 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.

  11. Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

    Science.gov (United States)

    Mostafa, Hesham

    2017-08-01

    Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.

  12. Spikes and matter inhomogeneities in massless scalar field models

    International Nuclear Information System (INIS)

    Coley, A A; Lim, W C

    2016-01-01

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

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

    Science.gov (United States)

    Ponulak, Filip; Kasinski, Andrzej

    2011-01-01

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

  14. SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

    Science.gov (United States)

    Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

    2017-01-01

    This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

  15. 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...

  16. 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.

  17. pH-induced conformational change of the rotavirus VP4 spike: implications for cell entry and antibody neutralization.

    Science.gov (United States)

    Pesavento, Joseph B; Crawford, Sue E; Roberts, Ed; Estes, Mary K; Prasad, B V Venkataram

    2005-07-01

    The rotavirus spike protein, VP4, is a major determinant of infectivity and neutralization. Previously, we have shown that trypsin-enhanced infectivity of rotavirus involves a transformation of the VP4 spike from a flexible to a rigid bilobed structure. Here we show that at elevated pH the spike undergoes a drastic, irreversible conformational change and becomes stunted, with a pronounced trilobed appearance. These particles with altered spikes, at a normal pH of 7.5, despite the loss of infectivity and the ability to hemagglutinate, surprisingly exhibit sialic acid (SA)-independent cell binding in contrast to the SA-dependent cell binding exhibited by native virions. Remarkably, a neutralizing monoclonal antibody that remains bound to spikes throughout the pH changes (pH 7 to 11 and back to pH 7) completely prevents this conformational change, preserving the SA-dependent cell binding and hemagglutinating functions of the virion. A hypothesis that emerges from the present study is that high-pH treatment triggers a conformational change that mimics a post-SA-attachment step to expose an epitope recognized by a downstream receptor in the rotavirus cell entry process. This process involves sequential interactions with multiple receptors, and the mechanism by which the antibody neutralizes is by preventing this conformational change.

  18. Dynamics of bacterial communities in two unpolluted soils after spiking with phenanthrene: soil type specific and common responders

    Directory of Open Access Journals (Sweden)

    Guo-Chun eDing

    2012-08-01

    Full Text Available Considering their key role for ecosystem processes, it is important to understand the response of microbial communities in unpolluted soils to pollution with polycyclic aromatic hydrocarbons (PAH. Phenanthrene, a model compound for PAH, was spiked to a Cambisol and a Luvisol soil. Total community DNA from phenanthrene-spiked and control soils collected on days 0, 21 and 63 were analyzed based on PCR-amplified 16S rRNA genefragments. Denaturing gradient gel electrophoresis (DGGE fingerprints of bacterial communities increasingly deviated with time between spiked and control soils. In taxon specific DGGE, significant responses of Alphaproteobacteria and Actinobacteria became only detectable after 63 days, while significant effects on Betaproteobacteria were detectable in both soils after 21 days. Comparison of the taxonomic distribution of bacteria in spiked and control soils on day 63 as revealed by pyrosequencing indicated soil type specific negative effects of phenanthrene on several taxa, many of them belonging to the Gamma-, Beta- or Deltaproteobacteria. Bacterial richness and evenness decreased in spiked soils. Despite the significant differences in the bacterial community structure between both soils on day 0, similar genera increased in relative abundance after PAH spiking, especially Sphingomonas and Polaromonas. However, this did not result in an increased overall similarity of the bacterial communities in both soils.

  19. 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.

  20. Semi-automated analysis of EEG spikes in the preterm fetal sheep using wavelet analysis

    International Nuclear Information System (INIS)

    Walbran, A.C.; Unsworth, C.P.; Gunn, A.J.; Benett, L.

    2010-01-01

    Full text: Presentation Preference Oral Presentation Perinatal hypoxia plays a key role in the cause of brain injury in premature infants. Cerebral hypothermia commenced in the latent phase of evolving injury (first 6-8 h post hypoxic-ischemic insult) is the lead candidate for treatment however currently there is no means to identify which infants can benefit from treatment. Recent studies suggest that epileptiform transients in latent phase are predictive of neural outcome. To quantify this, an automated means of EEG analysis is required as EEG monitoring produces vast amounts of data which is timely to analyse manually. We have developed a semi-automated EEG spike detection method which employs a discretized version of the continuous wavelet transform (CWT). EEG data was obtained from a fetal sheep at approximately 0.7 of gestation. Fetal asphyxia was maintained for 25 min and the EEG recorded for 8 h before and after asphyxia. The CWT was calculated followed by the power of the wavelet transform coefficients. Areas of high power corresponded to spike waves so thresholding was employed to identify the spikes. The performance of the method was found have a good sensitivity and selectivity, thus demonstrating that this method is a simple, robust and potentially effective spike detection algorithm.

  1. Spike-based population coding and working memory.

    Directory of Open Access Journals (Sweden)

    Martin Boerlin

    2011-02-01

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

  2. Consensus-Based Sorting of Neuronal Spike Waveforms.

    Science.gov (United States)

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

    2016-01-01

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

  3. Preserving Digital Materials

    CERN Document Server

    Harvey, Ross

    2011-01-01

    This book provides a single-volume introduction to the principles, strategies and practices currently applied by librarians and recordkeeping professionals to the critical issue of preservation of digital information. It incorporates practice from both the recordkeeping and the library communities, taking stock of current knowledge about digital preservation and describing recent and current research, to provide a framework for reflecting on the issues that digital preservation raises in professional practice.

  4. Timing and preservation mechanism of deglacial pteropod spike from the Andaman Sea, northeastern Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Sijinkumar, A.V.; Nath, B.N.; Gupta, M.V.S.; Rao, B.R.

    system in the Arabian Sea. Deep-Sea Research II 45, 2225-2252. Milliman, J. S. & Meade, R. H. 1983: World-wide delivery of river sediment to the oceans. Journal of Geology 91, 1-21. Naqvi, W. A., Charles, C. D. & Fairbanks, R. G. 1994: Carbon...

  5. 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.

  6. 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

  7. 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.

  8. Communication through resonance in spiking neuronal networks.

    Science.gov (United States)

    Hahn, Gerald; Bujan, Alejandro F; Frégnac, Yves; Aertsen, Ad; Kumar, Arvind

    2014-08-01

    The cortex processes stimuli through a distributed network of specialized brain areas. This processing requires mechanisms that can route neuronal activity across weakly connected cortical regions. Routing models proposed thus far are either limited to propagation of spiking activity across strongly connected networks or require distinct mechanisms that create local oscillations and establish their coherence between distant cortical areas. Here, we propose a novel mechanism which explains how synchronous spiking activity propagates across weakly connected brain areas supported by oscillations. In our model, oscillatory activity unleashes network resonance that amplifies feeble synchronous signals and promotes their propagation along weak connections ("communication through resonance"). The emergence of coherent oscillations is a natural consequence of synchronous activity propagation and therefore the assumption of different mechanisms that create oscillations and provide coherence is not necessary. Moreover, the phase-locking of oscillations is a side effect of communication rather than its requirement. Finally, we show how the state of ongoing activity could affect the communication through resonance and propose that modulations of the ongoing activity state could influence information processing in distributed cortical networks.

  9. Environmental education on wood preservatives and preservative ...

    African Journals Online (AJOL)

    The development and use of wood preservatives in Nigeria should address not only the cost and demand functions but also the potential hazards in environmental equations. Forest products specialists are often asked about the perceived risks and environmental costs of treated wood products. Evidently, the civil society is ...

  10. A spiking neuron circuit based on a carbon nanotube transistor

    International Nuclear Information System (INIS)

    Chen, C-L; Kim, K; Truong, Q; Shen, A; Li, Z; Chen, Y

    2012-01-01

    A spiking neuron circuit based on a carbon nanotube (CNT) transistor is presented in this paper. The spiking neuron circuit has a crossbar architecture in which the transistor gates are connected to its row electrodes and the transistor sources are connected to its column electrodes. An electrochemical cell is incorporated in the gate of the transistor by sandwiching a hydrogen-doped poly(ethylene glycol)methyl ether (PEG) electrolyte between the CNT channel and the top gate electrode. An input spike applied to the gate triggers a dynamic drift of the hydrogen ions in the PEG electrolyte, resulting in a post-synaptic current (PSC) through the CNT channel. Spikes input into the rows trigger PSCs through multiple CNT transistors, and PSCs cumulate in the columns and integrate into a ‘soma’ circuit to trigger output spikes based on an integrate-and-fire mechanism. The spiking neuron circuit can potentially emulate biological neuron networks and their intelligent functions. (paper)

  11. The local field potential reflects surplus spike synchrony

    DEFF Research Database (Denmark)

    Denker, Michael; Roux, Sébastien; Lindén, Henrik

    2011-01-01

    While oscillations of the local field potential (LFP) are commonly attributed to the synchronization of neuronal firing rate on the same time scale, their relationship to coincident spiking in the millisecond range is unknown. Here, we present experimental evidence to reconcile the notions...... of synchrony at the level of spiking and at the mesoscopic scale. We demonstrate that only in time intervals of significant spike synchrony that cannot be explained on the basis of firing rates, coincident spikes are better phase locked to the LFP than predicted by the locking of the individual spikes....... This effect is enhanced in periods of large LFP amplitudes. A quantitative model explains the LFP dynamics by the orchestrated spiking activity in neuronal groups that contribute the observed surplus synchrony. From the correlation analysis, we infer that neurons participate in different constellations...

  12. 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.

  13. 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.

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

    Science.gov (United States)

    de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A; Navas, Adrian; Villacorta-Atienza, Jose A

    2015-01-01

    Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

  15. 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.

  16. 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.

  17. New Insights into Amino Acid Preservation in the Early Oceans Using Modern Analytical Techniques

    Science.gov (United States)

    Parker, Eric T.; Brinton, Karen L.; Burton, Aaron S.; Glavin, Daniel P.; Dworkin, Jason P.; Bada, Jeffrey L.

    2015-01-01

    Protein- and non-protein-amino acids likely occupied the oceans at the time of the origin and evolution of life. Primordial soup-, hydrothermal vent-, and meteoritic-processes likely contributed to this early chemical inventory. Prebiotic synthesis and carbonaceous meteorite studies suggest that non-protein amino acids were likely more abundant than their protein-counterparts. Amino acid preservation before abiotic and biotic destruction is key to biomarker availability in paleoenvironments and remains an important uncertainty. To constrain primitive amino acid lifetimes, a 1992 archived seawater/beach sand mixture was spiked with D,L-alanine, D,L-valine (Val), alpha-aminoisobutyric acid (alpha-AIB), D,L-isovaline (Iva), and glycine (Gly). Analysis by high performance liquid chromatography with fluorescence detection (HPLC-FD) showed that only D-Val and non-protein amino acids were abundant after 2250 days. The mixture was re-analyzed in 2012 using HPLC-FD and a triple quadrupole mass spectrometer (QqQ-MS). The analytical results 20 years after the inception of the experiment were strikingly similar to those after 2250 days. To confirm that viable microorganisms were still present, the mixture was re-spiked with Gly in 2012. Aliquots were collected immediately after spiking, and at 5- and 9-month intervals thereafter. Final HPLC-FD/QqQ-MS analyses were performed in 2014. The 2014 analyses revealed that only alpha-AIB, D,L-Iva, and D-Val remained abundant. The disappearance of Gly indicated that microorganisms still lived in the mixture and were capable of consuming protein amino acids. These findings demonstrate that non-protein amino acids are minimally impacted by biological degradation and thus have very long lifetimes under these conditions. Primitive non-protein amino acids from terrestrial synthesis, or meteorite in-fall, likely experienced great-er preservation than protein amino acids in paleo-oceanic environments. Such robust molecules may have reached a

  18. Mass preserving image registration

    DEFF Research Database (Denmark)

    Gorbunova, Vladlena; Sporring, Jon; Lo, Pechin Chien Pau

    2010-01-01

    The paper presents results the mass preserving image registration method in the Evaluation of Methods for Pulmonary Image Registration 2010 (EMPIRE10) Challenge. The mass preserving image registration algorithm was applied to the 20 image pairs. Registration was evaluated using four different...

  19. Modes of fossil preservation

    Science.gov (United States)

    Schopf, J.M.

    1975-01-01

    The processes of geologic preservation are important for understanding the organisms represented by fossils. Some fossil differences are due to basic differences in organization of animals and plants, but the interpretation of fossils has also tended to be influenced by modes of preservation. Four modes of preservation generally can be distinguished: (1) Cellular permineralization ("petrifaction") preserves anatomical detail, and, occasionally, even cytologic structures. (2) Coalified compression, best illustrated by structures from coal but characteristic of many plant fossils in shale, preserves anatomical details in distorted form and produces surface replicas (impressions) on enclosing matrix. (3) Authigenic preservation replicates surface form or outline (molds and casts) prior to distortion by compression and, depending on cementation and timing, may intergrade with fossils that have been subject to compression. (4) Duripartic (hard part) preservation is characteristic of fossil skeletal remains, predominantly animal. Molds, pseudomorphs, or casts may form as bulk replacements following dissolution of the original fossil material, usually by leaching. Classification of the kinds of preservation in fossils will aid in identifying the processes responsible for modifying the fossil remains of both animals and plants. ?? 1975.

  20. Food preservation by irradiation

    International Nuclear Information System (INIS)

    Labots, H.; Huis in 't Veld, G.J.P.; Verrips, C.T.

    1985-01-01

    After a review of several methods for the preservation of food and the routes of food infections, the following chapters are devoted to the preservation by irradiation. Applications and legal aspects of food irradiation are described. Special reference is made to the international situation. (Auth.)

  1. Generalized activity equations for spiking neural network dynamics

    Directory of Open Access Journals (Sweden)

    Michael A Buice

    2013-11-01

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

  2. In-reactor creep of zirconium alloys by thermal spikes

    International Nuclear Information System (INIS)

    Ibrahim, E.F.

    1975-01-01

    The size and duration of thermal spikes from fast neutrons have been calculated for zirconium alloys, showing that spikes up to 1.8 nm radius may exist for 2 x 10 -11 s at greater than melting point, at 570K ambient temperature. Creep rates have been calculated assuming that the elastic strain from the applied stress relaxes in the volume of the spikes (by preferential loop alignment or modification of an existing dislocation network). The calculated rates are consistent with strain rates observed in long term tests-in-reactor, if spike lifetimes are 2 to 2.5 x 10 -11 s. (Auth.)

  3. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.

    Science.gov (United States)

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

    Network of neurons in the brain apply-unlike processors in our current generation of computer hardware-an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling.

  4. Surfing a spike wave down the ventral stream.

    Science.gov (United States)

    VanRullen, Rufin; Thorpe, Simon J

    2002-10-01

    Numerous theories of neural processing, often motivated by experimental observations, have explored the computational properties of neural codes based on the absolute or relative timing of spikes in spike trains. Spiking neuron models and theories however, as well as their experimental counterparts, have generally been limited to the simulation or observation of isolated neurons, isolated spike trains, or reduced neural populations. Such theories would therefore seem inappropriate to capture the properties of a neural code relying on temporal spike patterns distributed across large neuronal populations. Here we report a range of computer simulations and theoretical considerations that were designed to explore the possibilities of one such code and its relevance for visual processing. In a unified framework where the relation between stimulus saliency and spike relative timing plays the central role, we describe how the ventral stream of the visual system could process natural input scenes and extract meaningful information, both rapidly and reliably. The first wave of spikes generated in the retina in response to a visual stimulation carries information explicitly in its spatio-temporal structure: the most salient information is represented by the first spikes over the population. This spike wave, propagating through a hierarchy of visual areas, is regenerated at each processing stage, where its temporal structure can be modified by (i). the selectivity of the cortical neurons, (ii). lateral interactions and (iii). top-down attentional influences from higher order cortical areas. The resulting model could account for the remarkable efficiency and rapidity of processing observed in the primate visual system.

  5. Self-preserving cosmetics.

    Science.gov (United States)

    Varvaresou, A; Papageorgiou, S; Tsirivas, E; Protopapa, E; Kintziou, H; Kefala, V; Demetzos, C

    2009-06-01

    Preservatives are added to products for two reasons: first, to prevent microbial spoilage and therefore to prolong the shelf life of the product; second, to protect the consumer from a potential infection. Although chemical preservatives prevent microbial growth, their safety is questioned by a growing segment of consumers. Therefore, there is a considerable interest in the development of preservative-free or self-preserving cosmetics. In these formulations traditional/chemical preservatives have been replaced by other cosmetic ingredients with antimicrobial properties that are not legislated as preservatives according to the Annex VI of the Commission Directive 76/768/EEC and the amending directives (2003/15/EC, 2007/17/EC and 2007/22/EC). 'Hurdle Technology', a technology that has been used for the control of product safety in the food industry since 1970s, has also been applied for the production of self-preserving cosmetics. 'Hurdle Technology' is a term used to describe the intelligent combination of different preservation factors or hurdles to deteriorate the growth of microorganisms. Adherence to current good manufacturing practice, appropriate packaging, careful choice of the form of the emulsion, low water activity and low or high pH values are significant variables for the control of microbial growth in cosmetic formulations. This paper describes the application of the basic principles of 'Hurdle Technology' in the production of self-preserving cosmetics. Multifunctional antimicrobial ingredients and plant-derived essential oils and extracts that are used as alternative or natural preservatives and are not listed in Annex VI of the Cosmetic Directive are also reported.

  6. Influence of soil γ-irradiation and spiking on sorption of p,p'-DDE and soil organic matter chemistry.

    Science.gov (United States)

    Škulcová, Lucia; Scherr, Kerstin E; Chrást, Lukáš; Hofman, Jakub; Bielská, Lucie

    2018-07-15

    The fate of organic chemicals and their metabolites in soils is often investigated in model matrices having undergone various pre-treatment steps that may qualitatively or quantitatively interfere with the results. Presently, effects associated with soil sterilization by γ-irradiation and soil spiking using an organic solvent were studied in one freshly spiked soil (sterilization prior to contamination) and its field-contaminated (sterilization after contamination) counterpart for the model organic compound 1,1-Dichloro-2,2-bis(4-chlorophenyl)ethene (p,p'-DDE). Changes in the sorption and potential bioavailability of spiked and native p,p'-DDE were measured by supercritical fluid extraction (SFE), XAD-assisted extraction (XAD), and solid-phase microextraction (SPME) and linked to qualitative changes in soil organic matter (SOM) chemistry measured by diffuse reflectance infrared Fourier-transform (DRIFT) spectroscopy. Reduced sorption of p,p´-DDE detected with XAD and SPME was associated more clearly with spiking than with sterilization, but SFE showed a negligible impact. Spiking resulted in an increase of the DRIFT-derived hydrophobicity index, but irradiation did not. Spectral peak height ratio descriptors indicated increasing hydrophobicity and hydrophilicity in pristine soil following sterilization, and a greater reduction of hydrophobic over hydrophilic groups as a consequence of spiking. In parallel, reduced sorption of p,p´-DDE upon spiking was observed. Based on the present samples, γ-irradiation appears to alter soil sorptive properties to a lesser extent when compared to common laboratory processes such as spiking with organic solvents. Copyright © 2018. Published by Elsevier Inc.

  7. 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.

  8. An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

    Science.gov (United States)

    Chen, Huan-Yuan; Chen, Chih-Chang

    2017-01-01

    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. PMID:28956859

  9. 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

  10. Self-control with spiking and non-spiking neural networks playing games.

    Science.gov (United States)

    Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

    2010-01-01

    Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the

  11. Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices.

    Science.gov (United States)

    Zarudnyi, Konstantin; Mehonic, Adnan; Montesi, Luca; Buckwell, Mark; Hudziak, Stephen; Kenyon, Anthony J

    2018-01-01

    Resistance switching, or Resistive RAM (RRAM) devices show considerable potential for application in hardware spiking neural networks (neuro-inspired computing) by mimicking some of the behavior of biological synapses, and hence enabling non-von Neumann computer architectures. Spike-timing dependent plasticity (STDP) is one such behavior, and one example of several classes of plasticity that are being examined with the aim of finding suitable algorithms for application in many computing tasks such as coincidence detection, classification and image recognition. In previous work we have demonstrated that the neuromorphic capabilities of silicon-rich silicon oxide (SiO x ) resistance switching devices extend beyond plasticity to include thresholding, spiking, and integration. We previously demonstrated such behaviors in devices operated in the unipolar mode, opening up the question of whether we could add plasticity to the list of features exhibited by our devices. Here we demonstrate clear STDP in unipolar devices. Significantly, we show that the response of our devices is broadly similar to that of biological synapses. This work further reinforces the potential of simple two-terminal RRAM devices to mimic neuronal functionality in hardware spiking neural networks.

  12. [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.

  13. A propane price spike nails users

    International Nuclear Information System (INIS)

    Milke, M.

    1997-01-01

    The increase in price for propane was discussed. In 1993, propane cost about 5 cents per litre; by December 1996, the price has risen to 27 cents wholesale, while retail prices for auto propane reached 40 cents per litre. As a result, farmers and fleet operators are considering switching to an alternative energy supply. The five factors which may have played a role in the propane price spike were described. These included a cold winter which lowered inventories, a Pemex gas plant in Mexico which had been damaged by fire, forcing Mexico to import natural gas and natural gas liquids from the USA, the failure of propane distributors to restock during the summer months in the hope of lower prices, and increased cost of competing fuels in the face of increased demand. It was noted that these factors are transitory, which could mean better prices this summer

  14. 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.

  15. A simple procedure for the extraction of DNA from long-term formalin-preserved brain tissues for the detection of EBV by PCR.

    Science.gov (United States)

    Hassani, Asma; Khan, Gulfaraz

    2015-12-01

    Long-term formalin fixed brain tissues are potentially an important source of material for molecular studies. Ironically, very few protocols have been published describing DNA extraction from such material for use in PCR analysis. In our attempt to investigate the role of Epstein-Barr virus (EBV) in the pathogenesis of multiple sclerosis (MS), extracting PCR quality DNA from brain samples fixed in formalin for 2-22 years, proved to be very difficult and challenging. As expected, DNA extracted from these samples was not only of poor quality and quantity, but more importantly, it was frequently found to be non-amplifiable due to the presence of PCR inhibitors. Here, we describe a simple and reproducible procedure for extracting DNA using a modified proteinase K and phenol-chloroform methodology. Central to this protocol is the thorough pre-digestion washing of the tissues in PBS, extensive digestion with proteinase K in low SDS containing buffer, and using low NaCl concentration during DNA precipitation. The optimized protocol was used in extracting DNA from meninges of 26 MS and 6 non-MS cases. Although the quality of DNA from these samples was generally poor, small size amplicons (100-200 nucleotides) of the house-keeping gene, β-globin could be reliably amplified from all the cases. PCR for EBV revealed positivity in 35% (9/26) MS cases, but 0/6 non-MS cases. These findings indicate that the method described here is suitable for PCR detection of viral sequences in long-term formalin persevered brain tissues. Our findings also support a possible role for EBV in the pathogenesis of MS. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Chronopolis Digital Preservation Network

    Directory of Open Access Journals (Sweden)

    David Minor

    2010-07-01

    Full Text Available The Chronopolis Digital Preservation Initiative, one of the Library of Congress’ latest efforts to collect and preserve at-risk digital information, has completed its first year of service as a multi-member partnership to meet the archival needs of a wide range of domains.Chronopolis is a digital preservation data grid framework developed by the San Diego Supercomputer Center (SDSC at UC San Diego, the UC San Diego Libraries (UCSDL, and their partners at the National Center for Atmospheric Research (NCAR in Colorado and the University of Maryland's Institute for Advanced Computer Studies (UMIACS.Chronopolis addresses a critical problem by providing a comprehensive model for the cyberinfrastructure of collection management, in which preserved intellectual capital is easily accessible, and research results, education material, and new knowledge can be incorporated smoothly over the long term. Integrating digital library, data grid, and persistent archive technologies, Chronopolis has created trusted environments that span academic institutions and research projects, with the goal of long-term digital preservation.A key goal of the Chronopolis project is to provide cross-domain collection sharing for long-term preservation. Using existing high-speed educational and research networks and mass-scale storage infrastructure investments, the partnership is leveraging the data storage capabilities at SDSC, NCAR, and UMIACS to provide a preservation data grid that emphasizes heterogeneous and highly redundant data storage systems.In this paper we will explore the major themes within Chronopolis, including:a The philosophy and theory behind a nationally federated data grid for preservation. b The core tools and technologies used in Chronopolis. c The metadata schema that is being developed within Chronopolis for all of the data elements. d Lessons learned from the first year of the project.e Next steps in digital preservation using Chronopolis: how we

  17. 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.

  18. Simultaneous speciation and preservation of aqueous As, Sb and Se redox couples

    Science.gov (United States)

    Wu, D.; Pichler, T.

    2014-12-01

    We developed a new method for the simultaneous speciation analysis of inorganic arsenic (III, V), antimony (III, V) and selenium (IV, VI) in water samples via double-focusing sector field-inductively coupled plasma-mass spectrometry (SF-ICP-MS) coupled to high performance liquid chromatography (HPLC). A Hamilton PRX-X100 anion exchange column with EDTA (pH of 4.7) and 3% methanol as mobile phase was used for species separation. The flow rate was set to 1.5 mL min-1 and a solvent gradient (linear ramp from 5 mM to 30 mM) was applied. The overall analysis time for all six desired species was 11 minutes. The detection limits for As(III), As(V), Sb(III), Sb(V), Se(VI) and Se(IV) were 0.02 μg L-1, 0.06 μg L-1, 0.2 μg L-1, 0.02 μg L-1, 0.2 μg L-1 and 0.4 μg L-1 respectively. The retention times for As(III), As(V), Sb(III), Sb(V), Se(IV) and Se(VI) were 1.70, 2.94, 7.14, 2.28, 3.38 and 9.36 min, respectively. Subsequently, the stability of inorganic As(III, V), Sb(III, V) and Se(IV, VI) species in different water samples (groundwater, lake water and river water) was studied over a time scale of 11 weeks. High concentrations of Fe (25.0 mg/L) and Mn (25.0 mg/L) were added to different matrices to simulate Fe and Mn rich environments. All samples were spiked with 5.0 μg/L As(III, V) and Sb(III, V) and 15.0 μg/L Se(IV, VI).. We investigated several strategies for species preservation, i.e., EDTA only, EDTA combined with acidification (HCl, HNO3, formic acid and acetic acid). The preserved samples were stored at 4 °C in the dark. For comparison, another subsample without any preservation was stored at room temperature in the presence of light. The results showed that a combination EDTA acidified to pH of 3 can be used to preserve all species for at least 11 weeks. While EDTA only (pH = 6) failed to preserve As and Sb species, although Se species were preserved.

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

    DEFF Research Database (Denmark)

    Proietti, Tomasso; Haldrup, Niels; Knapik, Oskar

    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...

  20. The Nature of Power Spikes: a regime-switch approach

    NARCIS (Netherlands)

    C.M. de Jong (Cyriel)

    2005-01-01

    textabstractDue to its non-storable nature, electricity is a commodity with probably the most volatile spot prices, exemplified by occasional spikes. Appropriate pricing, portfolio, and risk management models have to incorporate these characteristics, and the spikes in particular. We investigate the

  1. No WIMP mini-spikes in dwarf spheroidal galaxies

    NARCIS (Netherlands)

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

    2015-01-01

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

  2. 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.

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

    DEFF Research Database (Denmark)

    Kardos, J; Szilágyi, N; Juhász, G

    1998-01-01

    . Delayed complex responses of large [Ca2+]c spiking observed in cells from a different set of cultures were synthesized by a set of frequencies within the range 0.018-0.117 Hz. Differential frequency patterns are suggested as characteristics of the [Ca2+]c spiking responses of neurons under different...

  4. Accelerated spike resampling for accurate multiple testing controls.

    Science.gov (United States)

    Harrison, Matthew T

    2013-02-01

    Controlling for multiple hypothesis tests using standard spike resampling techniques often requires prohibitive amounts of computation. Importance sampling techniques can be used to accelerate the computation. The general theory is presented, along with specific examples for testing differences across conditions using permutation tests and for testing pairwise synchrony and precise lagged-correlation between many simultaneously recorded spike trains using interval jitter.

  5. Causal Inference and Explaining Away in a Spiking Network

    Science.gov (United States)

    Moreno-Bote, Rubén; Drugowitsch, Jan

    2015-01-01

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

  6. Cytoplasmic tail of coronavirus spike protein has intracellular

    Indian Academy of Sciences (India)

    https://www.ias.ac.in/article/fulltext/jbsc/042/02/0231-0244. Keywords. Coronavirus spike protein trafficking; cytoplasmic tail signal; endoplasmic reticulum–Golgi intermediate complex; lysosome. Abstract. Intracellular trafficking and localization studies of spike protein from SARS and OC43 showed that SARS spikeprotein is ...

  7. Optimization of preservation activities and preservation engineering (1)

    International Nuclear Information System (INIS)

    Aoki, Takayuki; Mimaki, Hidehito; Oda, Mitsuyuki

    2004-01-01

    In order to deal with the optimization of preservation activities and 'preservation engineering' which makes it possible, the relation between general society and preservation, the content and the structure of preservation activities, and the viewpoint and the approach of the optimization of the preventive preservation are described. The optimization of the preventive preservation is shown respectively in the four stages of planning, implementation, result evaluation and countermeasure. (K. Kato)

  8. VT Historic Preservation Grant

    Data.gov (United States)

    Vermont Center for Geographic Information — The State-funded Historic Preservation Grant Program helps municipalities and non-profit organizations rehabilitate the historic buildings that are a vital part of...

  9. Preservation of Built Environments

    DEFF Research Database (Denmark)

    Pilegaard, Marie Kirstine

    When built environments and recently also cultural environments are to be preserved, the historic and architectural values are identified as the key motivations. In Denmark the SAVE system is used as a tool to identify architectural values, but in recent years it has been criticized for having...... architectural value in preservation work as a matter of maintaining the buildings -as keeping them "alive" and allowing this to continue in the future. The predominantly aesthetic preservation approach will stop the buildings' life process, which is the same as - "letting them die". Finnebyen in Aarhus...... is an example of a residential area, where the planning authority currently has presented a preservational district plan, following guidelines from the SAVE method. The purpose is to protect the area's architectural values in the future. The predominantly aesthetic approach is here used coupled to the concept...

  10. Radiation preservation of maize

    International Nuclear Information System (INIS)

    Wasito.

    1980-01-01

    Radiation preservation of maize was carried out. Radiation doses and sources, shielding materials, packaging materials, chemical radiation effects, biological radiation effects, were discussed. Experimental methods, samples and accessories were also presented. (SMN)

  11. Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO2 and CH4

    Science.gov (United States)

    El Yazidi, Abdelhadi; Ramonet, Michel; Ciais, Philippe; Broquet, Gregoire; Pison, Isabelle; Abbaris, Amara; Brunner, Dominik; Conil, Sebastien; Delmotte, Marc; Gheusi, Francois; Guerin, Frederic; Hazan, Lynn; Kachroudi, Nesrine; Kouvarakis, Giorgos; Mihalopoulos, Nikolaos; Rivier, Leonard; Serça, Dominique

    2018-03-01

    This study deals with the problem of identifying atmospheric data influenced by local emissions that can result in spikes in time series of greenhouse gases and long-lived tracer measurements. We considered three spike detection methods known as coefficient of variation (COV), robust extraction of baseline signal (REBS) and standard deviation of the background (SD) to detect and filter positive spikes in continuous greenhouse gas time series from four monitoring stations representative of the European ICOS (Integrated Carbon Observation System) Research Infrastructure network. The results of the different methods are compared to each other and against a manual detection performed by station managers. Four stations were selected as test cases to apply the spike detection methods: a continental rural tower of 100 m height in eastern France (OPE), a high-mountain observatory in the south-west of France (PDM), a regional marine background site in Crete (FKL) and a marine clean-air background site in the Southern Hemisphere on Amsterdam Island (AMS). This selection allows us to address spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analysed. All methods were found to be able to detect short-term spikes (lasting from a few seconds to a few minutes) in the time series. Analysis of the results of each method leads us to exclude the COV method due to the requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or underestimate the actual number of spikes. The two other methods freely determine the number of spikes for a given set of parameters, and the values of these parameters were calibrated to provide the best match with spikes known to reflect local emissions episodes that are well documented by the station managers. More than 96 % of the spikes manually identified by station managers were successfully detected both in the SD and the

  12. Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO2 and CH4

    Directory of Open Access Journals (Sweden)

    A. El Yazidi

    2018-03-01

    Full Text Available This study deals with the problem of identifying atmospheric data influenced by local emissions that can result in spikes in time series of greenhouse gases and long-lived tracer measurements. We considered three spike detection methods known as coefficient of variation (COV, robust extraction of baseline signal (REBS and standard deviation of the background (SD to detect and filter positive spikes in continuous greenhouse gas time series from four monitoring stations representative of the European ICOS (Integrated Carbon Observation System Research Infrastructure network. The results of the different methods are compared to each other and against a manual detection performed by station managers. Four stations were selected as test cases to apply the spike detection methods: a continental rural tower of 100 m height in eastern France (OPE, a high-mountain observatory in the south-west of France (PDM, a regional marine background site in Crete (FKL and a marine clean-air background site in the Southern Hemisphere on Amsterdam Island (AMS. This selection allows us to address spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analysed. All methods were found to be able to detect short-term spikes (lasting from a few seconds to a few minutes in the time series. Analysis of the results of each method leads us to exclude the COV method due to the requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or underestimate the actual number of spikes. The two other methods freely determine the number of spikes for a given set of parameters, and the values of these parameters were calibrated to provide the best match with spikes known to reflect local emissions episodes that are well documented by the station managers. More than 96 % of the spikes manually identified by station managers were successfully detected both in

  13. Pressurized water reactor iodine spiking behavior under power transient conditions

    International Nuclear Information System (INIS)

    Ho, J.C.

    1992-01-01

    The most accepted theory explaining the cause of pressurized water reactor iodine spiking is steam formation and condensation in damaged fuel rods. The phase transformation of the primary coolant from water to steam and back again is believed to cause the iodine spiking phenomenon. But due to the complex nature of the phenomenon, a comprehensive model of the behavior has not yet been successfully developed. This paper presents a new model based on an empirical approach, which gives a first-order estimation of the peak iodine spiking magnitude. Based on the proposed iodine spiking model, it is apparent that it is feasible to derive a correlation using the plant operating data base to monitor and control the peak iodine spiking magnitude

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

    Science.gov (United States)

    Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier

    2016-11-01

    In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. 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.

  16. Digital preservation for heritages

    CERN Document Server

    Lu, Dongming

    2011-01-01

    ""Digital Preservation for Heritages: Technologies and Applications"" provides a comprehensive and up-to-date coverage of digital technologies in the area of cultural heritage preservation, including digitalization, research aiding, conservation aiding, digital exhibition, and digital utilization. Processes, technical frameworks, key technologies, as well as typical systems and applications are discussed in the book. It is intended for researchers and students in the fields of computer science and technology, museology, and archaeology. Dr. Dongming Lu is a professor at College of Computer Sci

  17. Indiana Pavement Preservation Program

    OpenAIRE

    Ong, Ghim Ping (Raymond); Nantung, Tommy E.; Sinha, Kumares C.

    2010-01-01

    State highway agencies are facing immense pressure to maintain roads at acceptable levels amidst the challenging financial and economic situations. In recent years, pavement preservation has been sought as a potential alternative for managing the pavement assets, believing that it would provide a cost-effective solution in maintaining infrastructural conditions and meeting user expectations. This study explores the potential of pavement preservation concepts in managing the agency‘s pavement ...

  18. Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling

    International Nuclear Information System (INIS)

    Janczura, Joanna; Trück, Stefan; Weron, Rafał; Wolff, Rodney C.

    2013-01-01

    An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatments of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification. - Highlights: • First comprehensive study on the impact of spikes on seasonal pattern estimation • The effects of different treatments of spikes on model estimation are examined. • Cleaning spot prices for outliers yields superior estimates of the seasonal pattern. • Removing outliers provides better parameter estimates for the stochastic process. • Rankings of filtering techniques suggested in the literature are provided

  19. Noisy Spiking in Visual Area V2 of Amblyopic Monkeys.

    Science.gov (United States)

    Wang, Ye; Zhang, Bin; Tao, Xiaofeng; Wensveen, Janice M; Smith, Earl L; Chino, Yuzo M

    2017-01-25

    Interocular decorrelation of input signals in developing visual cortex can cause impaired binocular vision and amblyopia. Although increased intrinsic noise is thought to be responsible for a range of perceptual deficits in amblyopic humans, the neural basis for the elevated perceptual noise in amblyopic primates is not known. Here, we tested the idea that perceptual noise is linked to the neuronal spiking noise (variability) resulting from developmental alterations in cortical circuitry. To assess spiking noise, we analyzed the contrast-dependent dynamics of spike counts and spiking irregularity by calculating the square of the coefficient of variation in interspike intervals (CV 2 ) and the trial-to-trial fluctuations in spiking, or mean matched Fano factor (m-FF) in visual area V2 of monkeys reared with chronic monocular defocus. In amblyopic neurons, the contrast versus response functions and the spike count dynamics exhibited significant deviations from comparable data for normal monkeys. The CV 2 was pronounced in amblyopic neurons for high-contrast stimuli and the m-FF was abnormally high in amblyopic neurons for low-contrast gratings. The spike count, CV 2 , and m-FF of spontaneous activity were also elevated in amblyopic neurons. These contrast-dependent spiking irregularities were correlated with the level of binocular suppression in these V2 neurons and with the severity of perceptual loss for individual monkeys. Our results suggest that the developmental alterations in normalization mechanisms resulting from early binocular suppression can explain much of these contrast-dependent spiking abnormalities in V2 neurons and the perceptual performance of our amblyopic monkeys. Amblyopia is a common developmental vision disorder in humans. Despite the extensive animal studies on how amblyopia emerges, we know surprisingly little about the neural basis of amblyopia in humans and nonhuman primates. Although the vision of amblyopic humans is often described as

  20. Asynchronous Rate Chaos in Spiking Neuronal Circuits.

    Directory of Open Access Journals (Sweden)

    Omri Harish

    2015-07-01

    Full Text Available The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results.

  1. Memory recall and spike-frequency adaptation

    Science.gov (United States)

    Roach, James P.; Sander, Leonard M.; Zochowski, Michal R.

    2016-05-01

    The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored patterns that contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model, this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations. Here we show that spike-frequency adaptation (SFA), a common mechanism affecting neuron activation in the brain, can provide state-dependent control of pattern retrieval. We demonstrate this in a Hopfield network modified to include SFA, and also in a model network of biophysical neurons. In both cases, SFA allows for selective stabilization of attractors with different basins of attraction, and also for temporal dynamics of attractor switching that is not possible in standard autoassociative schemes. The dynamics of our models give a plausible account of different sorts of memory retrieval.

  2. Phase diagram of spiking neural networks.

    Science.gov (United States)

    Seyed-Allaei, Hamed

    2015-01-01

    In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I systematically simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable. I stimulate networks with pulses and then measure their: dynamic range, dominant frequency of population activities, total duration of activities, maximum rate of population and the occurrence time of maximum rate. The results are organized in phase diagram. This phase diagram gives an insight into the space of parameters - excitatory to inhibitory ratio, sparseness of connections and synaptic weights. This phase diagram can be used to decide the parameters of a model. The phase diagrams show that networks which are configured according to the common values, have a good dynamic range in response to an impulse and their dynamic range is robust in respect to synaptic weights, and for some synaptic weights they oscillates in α or β frequencies, independent of external stimuli.

  3. Asynchronous Rate Chaos in Spiking Neuronal Circuits

    Science.gov (United States)

    Harish, Omri; Hansel, David

    2015-01-01

    The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results. PMID:26230679

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. Geomagnetic spikes on the core-mantle boundary

    Science.gov (United States)

    Davies, C. J.; Constable, C.

    2017-12-01

    Extreme variations of Earth's magnetic field occurred in the Levantine region around 1000 BC, where the field intensity rose and fell by a factor of 2-3 over a short time and confined spatial region. There is presently no coherent link between this intensity spike and the generating processes in Earth's liquid core. Here we test the attribution of a surface spike to a flux patch visible on the core-mantle boundary (CMB), calculating geometric and energetic bounds on resulting surface geomagnetic features. We show that the Levantine intensity high must span at least 60 degrees in longitude. Models providing the best trade-off between matching surface spike intensity, minimizing L1 and L2 misfit to the available data and satisfying core energy constraints produce CMB spikes 8-22 degrees wide with peak values of O(100) mT. We propose that the Levantine spike grew in place before migrating northward and westward, contributing to the growth of the axial dipole field seen in Holocene field models. Estimates of Ohmic dissipation suggest that diffusive processes, which are often neglected, likely govern the ultimate decay of geomagnetic spikes. Using these results, we search for the presence of spike-like features in geodynamo simulations.

  10. Training for Preservation Management

    Directory of Open Access Journals (Sweden)

    Mirjam M. Foot

    1999-05-01

    Full Text Available In August 1997 the first of a series of summer schools in Preservation Management was held at the Archivschule in Marburg (Germany. The school was organised by the ECPA, the LIBER Division on Preservation, ICA and the Archivschule itself and was aimed at archivists and librarians in management positions from European institutions. It dealt with managerial, organisational and financial aspects of preservation and required active participation by those attending. Apart from introductory sessions by the teaching staff at the Archivschule, a large part of the course took the form of working groups, discussions, assignments and role play, to which participants were expected to take their own experience and problems. The school was conducted in German. Topics, spread over five days, ranged from preservation in the context of the core activities of libraries and archives; planning of preservation projects; general management issues, such as resource management, budgeting, priority setting, communication and effecting change; to more detailed considerations of day-to-day issues, such as storage, disaster control, microfilming and digitising, mass conservation processes, and moulds and fungi.

  11. System-Level Design of a 64-Channel Low Power Neural Spike Recording Sensor.

    Science.gov (United States)

    Delgado-Restituto, Manuel; Rodriguez-Perez, Alberto; Darie, Angela; Soto-Sanchez, Cristina; Fernandez-Jover, Eduardo; Rodriguez-Vazquez, Angel

    2017-04-01

    This paper reports an integrated 64-channel neural spike recording sensor, together with all the circuitry to process and configure the channels, process the neural data, transmit via a wireless link the information and receive the required instructions. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration algorithm which individually configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by the embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330 μW.

  12. 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 ...

  13. Cancer and fertility preservation

    DEFF Research Database (Denmark)

    Lambertini, Matteo; Del Mastro, Lucia; Pescio, Maria C

    2016-01-01

    In the last years, thanks to the improvement in the prognosis of cancer patients, a growing attention has been given to the fertility issues. International guidelines on fertility preservation in cancer patients recommend that physicians discuss, as early as possible, with all patients...... of reproductive age their risk of infertility from the disease and/or treatment and their interest in having children after cancer, and help with informed fertility preservation decisions. As recommended by the American Society of Clinical Oncology and the European Society for Medical Oncology, sperm...... data have become available, and several issues in this field are still controversial and should be addressed by both patients and their treating physicians.In April 2015, physicians with expertise in the field of fertility preservation in cancer patients from several European countries were invited...

  14. Optimization of preservation activities and preservation engineering (2)

    International Nuclear Information System (INIS)

    Aoki, Takayuki; Mimaki, Hidehito; Oda, Mitsuyuki

    2004-01-01

    In order to deal with the optimization of preservation activities and 'preservation engineering' which makes it possible, the viewpoint and the approach of the optimization of the ex post facto preservation and the content to be possessed in 'preservation engineering' are described. The optimization of the ex post facto preservation is shown respectively in the four stages of planning, implementation, result evaluation and countermeasure. (K. Kato)

  15. Advanced Digital Preservation

    CERN Document Server

    Giaretta, David

    2011-01-01

    There is growing recognition of the need to address the fragility of digital information, on which our society heavily depends for smooth operation in all aspects of daily life. This has been discussed in many books and articles on digital preservation, so why is there a need for yet one more? Because, for the most part, those other publications focus on documents, images and webpages -- objects that are normally rendered to be simply displayed by software to a human viewer. Yet there are clearly many more types of digital objects that may need to be preserved, such as databases, scientific da

  16. Integrated workflows for spiking neuronal network simulations

    Directory of Open Access Journals (Sweden)

    Ján eAntolík

    2013-12-01

    Full Text Available The increasing availability of computational resources is enabling more detailed, realistic modelling in computational neuroscience, resulting in a shift towards more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeller's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modellers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity.To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualisation into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organised configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualisation stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modelling studies by relieving the user from manual handling of the flow of metadata between the individual

  17. 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.

  18. Radioisotopes and food preservation against insects

    International Nuclear Information System (INIS)

    Hachem Ahmad, M.S.

    1998-01-01

    The book describes how to preserve food from harmful insects by using radioisotopes. It focusses on the impact of ionized radiation on the different stages of insect growth and on its metabolism and immunity. It also discusses the relationship between radiation doses and insect reproduction. It explains the various methods to detect the irradiated foods

  19. 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

  20. Real-time computing platform for spiking neurons (RT-spike).

    Science.gov (United States)

    Ros, Eduardo; Ortigosa, Eva M; Agís, Rodrigo; Carrillo, Richard; Arnold, Michael

    2006-07-01

    A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important.

  1. 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.

  2. Timing intervals using population synchrony and spike timing dependent plasticity

    Directory of Open Access Journals (Sweden)

    Wei Xu

    2016-12-01

    Full Text Available We present a computational model by which ensembles of regularly spiking neurons can encode different time intervals through synchronous firing. We show that a neuron responding to a large population of convergent inputs has the potential to learn to produce an appropriately-timed output via spike-time dependent plasticity. We explain why temporal variability of this population synchrony increases with increasing time intervals. We also show that the scalar property of timing and its violation at short intervals can be explained by the spike-wise accumulation of jitter in the inter-spike intervals of timing neurons. We explore how the challenge of encoding longer time intervals can be overcome and conclude that this may involve a switch to a different population of neurons with lower firing rate, with the added effect of producing an earlier bias in response. Experimental data on human timing performance show features in agreement with the model’s output.

  3. 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...... origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access...... to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy...

  4. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan; Naous, Rawan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

    2015-01-01

    . 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

  5. Emergent dynamics of spiking neurons with fluctuating threshold

    Science.gov (United States)

    Bhattacharjee, Anindita; Das, M. K.

    2017-05-01

    Role of fluctuating threshold on neuronal dynamics is investigated. The threshold function is assumed to follow a normal probability distribution. Standard deviation of inter-spike interval of the response is computed as an indicator of irregularity in spike emission. It has been observed that, the irregularity in spiking is more if the threshold variation is more. A significant change in modal characteristics of Inter Spike Intervals (ISI) is seen to occur as a function of fluctuation parameter. Investigation is further carried out for coupled system of neurons. Cooperative dynamics of coupled neurons are discussed in view of synchronization. Total and partial synchronization regimes are depicted with the help of contour plots of synchrony measure under various conditions. Results of this investigation may provide a basis for exploring the complexities of neural communication and brain functioning.

  6. 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.

  7. Integral-preserving integrators

    International Nuclear Information System (INIS)

    McLaren, D I; Quispel, G R W

    2004-01-01

    Ordinary differential equations having a first integral may be solved numerically using one of several methods, with the integral preserved to machine accuracy. One such method is the discrete gradient method. It is shown here that the order of the method can be bootstrapped repeatedly to higher orders of accuracy. The method is illustrated using the Henon-Heiles system. (letter to the editor)

  8. Foodstuffs preservation by ionization

    International Nuclear Information System (INIS)

    1991-12-01

    This document contains all the papers presented at the meeting on foodstuffs preservation by ionization. These papers deal especially with the food ionization process, its development and the view of the food industry on ionization. Refs and figs (F.M.)

  9. Preserving food with radiation

    International Nuclear Information System (INIS)

    Thomas, A.C

    1978-01-01

    Food irradiation is becoming an increasingly more important method of food preservataion. The irradiation process and its advangages are briefly described, and its use in the preservation of poultry and various kinds of fruits is discussed. Fruit export is hampered by restrictions due to infestation. Radiation disinfestation will therefore be of great advantage and may lead to a growth in export markets

  10. Preserve America News

    Science.gov (United States)

    phone number. Whether or not you're able to let us know ahead of time, however, we hope you can join us Amache Preservation Society in Colorado and the Friends of Mount Hope Cemetery in New York. This brings Places: Breathing New Life into Our Communities." Read about this informative session. National

  11. POLARISATION PRESERVING OPTICAL FIBRE

    DEFF Research Database (Denmark)

    2000-01-01

    . This cladding structure provides polarisation preserving properties to the optical fibre. Optical fibres using this technology may have claddings with elements placed non-periodically as well as in a two-dimensional periodic lattice - such as cladding providing Photonic Band Gap (PBG) effects....

  12. Wood preservative testing

    Science.gov (United States)

    Rebecca Ibach; Stan T. Lebow

    2012-01-01

    Most wood species used in commercial and residential construction have little natural biological durability and will suffer from biodeterioration when exposed to moisture. Historically, this problem has been overcome by treating wood for outdoor use with toxic wood preservatives. As societal acceptance of chemical use changes, there is continual pressure to develop and...

  13. Preservation in New Buildings

    Directory of Open Access Journals (Sweden)

    Christopher Kitching

    2000-06-01

    Full Text Available In the United Kingdom (as in many other countries increasing attention is being paid to the importance of each library and archive having a written preservation strategy endorsed by its governing body. So increasingly we are asking: where does „preservation“ begin and what are its top priorities? Some would say preservation begins with the definition of collecting policies to ensure that only relevant items are acquired in the first place, and therefore that no unnecessary costs are incurred on the long-term care of unwanted and unconsulted items. Others might argue that the first priority must be the careful appraisal of existing holdings to determine their preservation and conservation requirements and to prioritise their treatment. Or should preservation begin with damage-limitation: restricting the physical handling of books and documents, on the one hand by providing whenever possible surrogate copies in digital formats or microform, and on the other hand by offering at least basic protection through appropriate boxing and packaging? This, surely, goes hand-in-hand with the education of staff and readers about the importance of treating rare or unique materials with proper respect.

  14. Preserving the Seminar Experience

    Science.gov (United States)

    Ramsey, David; Evans, Jocelyn; Levy, Meyer

    2016-01-01

    This article presents a new approach to online graduate education. With hopes of recruiting a larger cohort in order to preserve a graduate program struggling with low enrollment, we began offering a limited number of seats to students who would attend class in real time but from remote locations, using a videoconferencing platform. Unlike…

  15. Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

    Science.gov (United States)

    Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

    2015-07-15

    While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks

  16. A novel automated spike sorting algorithm with adaptable feature extraction.

    Science.gov (United States)

    Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

    2012-10-15

    To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

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

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

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-06

    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.

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

    Science.gov (United States)

    Kreuz, Thomas; Mulansky, Mario; Bozanic, Nebojsa

    2015-05-01

    Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. Copyright © 2015 the American Physiological Society.

  20. 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)

  1. DNA preservation in silk.

    Science.gov (United States)

    Liu, Yawen; Zheng, Zhaozhu; Gong, He; Liu, Meng; Guo, Shaozhe; Li, Gang; Wang, Xiaoqin; Kaplan, David L

    2017-06-27

    The structure of DNA is susceptible to alterations at high temperature and on changing pH, irradiation and exposure to DNase. Options to protect and preserve DNA during storage are important for applications in genetic diagnosis, identity authentication, drug development and bioresearch. In the present study, the stability of total DNA purified from human dermal fibroblast cells, as well as that of plasmid DNA, was studied in silk protein materials. The DNA/silk mixtures were stabilized on filter paper (silk/DNA + filter) or filter paper pre-coated with silk and treated with methanol (silk/DNA + PT-filter) as a route to practical utility. After air-drying and water extraction, 50-70% of the DNA and silk could be retrieved and showed a single band on electrophoretic gels. 6% silk/DNA + PT-filter samples provided improved stability in comparison with 3% silk/DNA + filter samples and DNA + filter samples for DNA preservation, with ∼40% of the band intensity remaining at 37 °C after 40 days and ∼10% after exposure to UV light for 10 hours. Quantitative analysis using the PicoGreen assay confirmed the results. The use of Tris/borate/EDTA (TBE) buffer enhanced the preservation and/or extraction of the DNA. The DNA extracted after storage maintained integrity and function based on serving as a functional template for PCR amplification of the gene for zinc finger protein 750 (ZNF750) and for transgene expression of red fluorescence protein (dsRed) in HEK293 cells. The high molecular weight and high content of a crystalline beta-sheet structure formed on the coated surfaces likely accounted for the preservation effects observed for the silk/DNA + PT-filter samples. Although similar preservation effects were also obtained for lyophilized silk/DNA samples, the rapid and simple processing available with the silk-DNA-filter membrane system makes it appealing for future applications.

  2. Orthobunyavirus ultrastructure and the curious tripodal glycoprotein spike.

    Directory of Open Access Journals (Sweden)

    Thomas A Bowden

    Full Text Available The genus Orthobunyavirus within the family Bunyaviridae constitutes an expanding group of emerging viruses, which threaten human and animal health. Despite the medical importance, little is known about orthobunyavirus structure, a prerequisite for understanding virus assembly and entry. Here, using electron cryo-tomography, we report the ultrastructure of Bunyamwera virus, the prototypic member of this genus. Whilst Bunyamwera virions are pleomorphic in shape, they display a locally ordered lattice of glycoprotein spikes. Each spike protrudes 18 nm from the viral membrane and becomes disordered upon introduction to an acidic environment. Using sub-tomogram averaging, we derived a three-dimensional model of the trimeric pre-fusion glycoprotein spike to 3-nm resolution. The glycoprotein spike consists mainly of the putative class-II fusion glycoprotein and exhibits a unique tripod-like arrangement. Protein-protein contacts between neighbouring spikes occur at membrane-proximal regions and intra-spike contacts at membrane-distal regions. This trimeric assembly deviates from previously observed fusion glycoprotein arrangements, suggesting a greater than anticipated repertoire of viral fusion glycoprotein oligomerization. Our study provides evidence of a pH-dependent conformational change that occurs during orthobunyaviral entry into host cells and a blueprint for the structure of this group of emerging pathogens.

  3. 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.

  4. 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.

  5. 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.

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

    Science.gov (United States)

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

    2014-06-01

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

  7. 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

  8. 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.

  9. Sound Rhythms Are Encoded by Postinhibitory Rebound Spiking in the Superior Paraolivary Nucleus

    Science.gov (United States)

    Felix, Richard A.; Fridberger, Anders; Leijon, Sara; Berrebi, Albert S.; Magnusson, Anna K.

    2013-01-01

    The superior paraolivary nucleus (SPON) is a prominent structure in the auditory brainstem. In contrast to the principal superior olivary nuclei with identified roles in processing binaural sound localization cues, the role of the SPON in hearing is not well understood. A combined in vitro and in vivo approach was used to investigate the cellular properties of SPON neurons in the mouse. Patch-clamp recordings in brain slices revealed that brief and well timed postinhibitory rebound spiking, generated by the interaction of two subthreshold-activated ion currents, is a hallmark of SPON neurons. The Ih current determines the timing of the rebound, whereas the T-type Ca2+ current boosts the rebound to spike threshold. This precisely timed rebound spiking provides a physiological explanation for the sensitivity of SPON neurons to sinusoidally amplitude-modulated (SAM) tones in vivo, where peaks in the sound envelope drive inhibitory inputs and SPON neurons fire action potentials during the waveform troughs. Consistent with this notion, SPON neurons display intrinsic tuning to frequency-modulated sinusoidal currents (1–15Hz) in vitro and discharge with strong synchrony to SAMs with modulation frequencies between 1 and 20 Hz in vivo. The results of this study suggest that the SPON is particularly well suited to encode rhythmic sound patterns. Such temporal periodicity information is likely important for detection of communication cues, such as the acoustic envelopes of animal vocalizations and speech signals. PMID:21880918

  10. Recording human cortical population spikes non-invasively--An EEG tutorial.

    Science.gov (United States)

    Waterstraat, Gunnar; Fedele, Tommaso; Burghoff, Martin; Scheer, Hans-Jürgen; Curio, Gabriel

    2015-07-30

    Non-invasively recorded somatosensory high-frequency oscillations (sHFOs) evoked by electric nerve stimulation are markers of human cortical population spikes. Previously, their analysis was based on massive averaging of EEG responses. Advanced neurotechnology and optimized off-line analysis can enhance the signal-to-noise ratio of sHFOs, eventually enabling single-trial analysis. The rationale for developing dedicated low-noise EEG technology for sHFOs is unfolded. Detailed recording procedures and tailored analysis principles are explained step-by-step. Source codes in Matlab and Python are provided as supplementary material online. Combining synergistic hardware and analysis improvements, evoked sHFOs at around 600 Hz ('σ-bursts') can be studied in single-trials. Additionally, optimized spatial filters increase the signal-to-noise ratio of components at about 1 kHz ('κ-bursts') enabling their detection in non-invasive surface EEG. sHFOs offer a unique possibility to record evoked human cortical population spikes non-invasively. The experimental approaches and algorithms presented here enable also non-specialized EEG laboratories to combine measurements of conventional low-frequency EEG with the analysis of concomitant cortical population spike responses. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Food preservation by irradiation

    International Nuclear Information System (INIS)

    Oztasiran, I.

    1984-01-01

    Irradiation is a physical process for treating food and as such it is comparable to other processing techniques such as heating or freezing foods for preservation. The energy level used in food irradiation is always below that producing radioactivity in the treated food, hence this aspect can be totally excluded in wholesomeness evaluations. Water is readily ionized and may be the primary source of ionization in foods with secondary effects on other molecules, possibly more a result of water ionization than of direct hits. In the presence of oxygen, highly reactive compounds may be produced, such as H, H 3 0+ and H 2 O 2 . Radiation at the energy flux levels used for food (<2 MeV) does not induce radioactivity. Food irradiation applications are already technically and economically feasible and that food so treated is suitable for consumption. Food irradiation techniques can play an important role for an improved preservation, storage and distribution of food products. (author)

  12. Food preservation by irradiation

    International Nuclear Information System (INIS)

    Kooij, J. van

    1981-01-01

    Twenty-five years of development work on the preservation of food by irradiation have shown that this technology has the potential to reduce post-harvest losses and to produce safe foods. The technological feasibility has been established but general acceptance of food irradiation by national regulatory bodies and consumers requires attention. The positive aspects of food preservation by irradiation include: the food keeps its freshness and its physical state, agents which cause spoilage (bacteria, etc.) are eliminated, recontamination does not take place, provided packaging materials are impermeable to bacteria and insects. It inhibits sprouting of root crops, kills insects and parasites, inactivates bacteria, spores and moulds, delays ripening of fruit, improves the technological properties of food. It makes foods biologically safe, allows the production of shelf-stable foods and is excellent for quarantine treatment, and generally improves food hygiene. The dose ranges needed for effective treatment are given

  13. Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks

    NARCIS (Netherlands)

    S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)

    2000-01-01

    textabstractWe demonstrate that spiking neural networks encoding information in spike times are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on

  14. How to preserve foods

    International Nuclear Information System (INIS)

    Balek, V.; Vadassova, J.

    1979-01-01

    The use of gamma and fast electron radiations for food preservation is described. Examples are given of the application of ionizing radiation for retarding potato germination, onion growth, and fruit ripening, for limiting the action of microorganisms, and removing salmonella from meat products. The method has remarkable prospects although it may not be considered to be a general-purpose method. Geographic and economic conditions should always be taken into consideration. (J.P.)

  15. Preservation and gamma irradiation

    International Nuclear Information System (INIS)

    Ramiere, R.

    1991-01-01

    The paper reviews briefly the application of gamma radiation to preservation of cultural objects for disinsectization, disinfection and strengthening of materials such as wood or stone by impregnation with a liquid resin and in situ polymerization. As heavy equipment is required two facilities are specialized a 1000 T Bq cobalt 60 source at Grenoble (France) and 100 T Bq one at Rostoky (Czechoslovakia). Examples of treated objects are given [fr

  16. Beneficial bread without preservatives

    OpenAIRE

    Denkova, Zapryana; Denkova, Rositsa

    2014-01-01

    Besides their inherent nutritional value functional foods contain substances that have beneficial impact on the functioning of organs and systems in the human body and reduce the risk of disease. Bread and bakery goods are basic foods in the diet of contemporary people. Preservatives are added to the composition of foods in order to ensure their microbiological safety, but these substances affect directly the balance of microflora in the tract. A great problem is mold and bacterial spoilage (...

  17. Fertility preservation 2

    Science.gov (United States)

    De Vos, Michel; Smitz, Johan; Woodruff, Teresa K

    2014-01-01

    Enhanced long-term survival rates of young women with cancer and advances in reproductive medicine and cryobiology have culminated in an increased interest in fertility preservation methods in girls and young women with cancer. Present data suggest that young patients with cancer should be referred for fertility preservation counselling quickly to help with their coping process. Although the clinical application of novel developments, including oocyte vitrification and oocyte maturation in vitro, has resulted in reasonable success rates in assisted reproduction programmes, experience with these techniques in the setting of fertility preservation is in its infancy. It is hoped that these and other approaches, some of which are still regarded as experimental (eg, ovarian tissue cryopreservation, pharmacological protection against gonadotoxic agents, in-vitro follicle growth, and follicle transplantation) will be optimised and become established within the next decade. Unravelling the complex mechanisms of activation and suppression of follicle growth will not only expand the care of thousands of women diagnosed with cancer, but also inform the care of millions of women confronted with reduced reproductive fitness because of ageing. PMID:25283571

  18. Grain preservation in SSSR

    International Nuclear Information System (INIS)

    Trisviatski, L.A.

    1973-01-01

    First the importance of cereals collected in the S.S.S.R., the reason why the government had to put in practice a storage chain, composed of large capacity store houses (200 000 metric tonnes, or more) is reminded. When climatic conditions result in wet harvested grains, cereals are dried either in state enterprise dryers (32 to 50 tonnes/hour) or in kolkhozes' dryers (2 to 16 tonnes/hour). A new type of drying with recycling, has been developped, economizing 10 to 15 p. 100. Then the possibilities offered by the technique of partial drying of very wet grains are studied and the preservation processes using fresh ventilation, or hot ventilation with drying effect are described. The question of silage of wet grains destined to animal consumption is then examined as well as preservation by sodium pyrosulfide; the use of propionic acid, little developped in SSSR, is studied now, just as storage with inert gas. The struggle technics against insects, either with chemical agents, or with irradiation are described. Finally the modalities of technicians formation, specialized in preservation, are discussed [fr

  19. 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.

  20. Dual roles for spike signaling in cortical neural populations

    Directory of Open Access Journals (Sweden)

    Dana eBallard

    2011-06-01

    Full Text Available A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning post-stimulus histograms and exponential interval histograms. In addition it makes testable predictions that follow from the γ latency coding.

  1. Lowering the quantification limit of the QubitTM RNA HS assay using RNA spike-in.

    Science.gov (United States)

    Li, Xin; Ben-Dov, Iddo Z; Mauro, Maurizio; Williams, Zev

    2015-05-06

    RNA quantification is often a prerequisite for most RNA analyses such as RNA sequencing. However, the relatively low sensitivity and large sample consumption of traditional RNA quantification methods such as UV spectrophotometry and even the much more sensitive fluorescence-based RNA quantification assays, such as the Qubit™ RNA HS Assay, are often inadequate for measuring minute levels of RNA isolated from limited cell and tissue samples and biofluids. Thus, there is a pressing need for a more sensitive method to reliably and robustly detect trace levels of RNA without interference from DNA. To improve the quantification limit of the Qubit™ RNA HS Assay, we spiked-in a known quantity of RNA to achieve the minimum reading required by the assay. Samples containing trace amounts of RNA were then added to the spike-in and measured as a reading increase over RNA spike-in baseline. We determined the accuracy and precision of reading increases between 1 and 20 pg/μL as well as RNA-specificity in this range, and compared to those of RiboGreen(®), another sensitive fluorescence-based RNA quantification assay. We then applied Qubit™ Assay with RNA spike-in to quantify plasma RNA samples. RNA spike-in improved the quantification limit of the Qubit™ RNA HS Assay 5-fold, from 25 pg/μL down to 5 pg/μL while maintaining high specificity to RNA. This enabled quantification of RNA with original concentration as low as 55.6 pg/μL compared to 250 pg/μL for the standard assay and decreased sample consumption from 5 to 1 ng. Plasma RNA samples that were not measurable by the Qubit™ RNA HS Assay were measurable by our modified method. The Qubit™ RNA HS Assay with RNA spike-in is able to quantify RNA with high specificity at 5-fold lower concentration and uses 5-fold less sample quantity than the standard Qubit™ Assay.

  2. Training development for pavement preservation.

    Science.gov (United States)

    2013-05-01

    This research project strives to help the Iowa Department of Transportation (DOT) fully achieve the full benefits of pavement : preservation through training on proper selection, design, and application of pavement preservation treatments. In some ca...

  3. ACHP | Tribal Historic Preservation Officers

    Science.gov (United States)

    preservation of significant historic properties. Those functions include identifying and maintaining Working with Section 106 Federal, State, & Tribal Programs Training & Education Publications Search skip specific nav links Home arrow Historic Preservation Programs & Officers arrow THPOs

  4. A metric space approach to the information capacity of spike trains

    OpenAIRE

    HOUGHTON, CONOR JAMES; GILLESPIE, JAMES

    2010-01-01

    PUBLISHED Classical information theory can be either discrete or continuous, corresponding to discrete or continuous random variables. However, although spike times in a spike train are described by continuous variables, the information content is usually calculated using discrete information theory. This is because the number of spikes, and hence, the number of variables, varies from spike train to spike train, making the continuous theory difficult to apply.It is possible to avoid ...

  5. Noise-enhanced coding in phasic neuron spike trains.

    Science.gov (United States)

    Ly, Cheng; Doiron, Brent

    2017-01-01

    The stochastic nature of neuronal response has lead to conjectures about the impact of input fluctuations on the neural coding. For the most part, low pass membrane integration and spike threshold dynamics have been the primary features assumed in the transfer from synaptic input to output spiking. Phasic neurons are a common, but understudied, neuron class that are characterized by a subthreshold negative feedback that suppresses spike train responses to low frequency signals. Past work has shown that when a low frequency signal is accompanied by moderate intensity broadband noise, phasic neurons spike trains are well locked to the signal. We extend these results with a simple, reduced model of phasic activity that demonstrates that a non-Markovian spike train structure caused by the negative feedback produces a noise-enhanced coding. Further, this enhancement is sensitive to the timescales, as opposed to the intensity, of a driving signal. Reduced hazard function models show that noise-enhanced phasic codes are both novel and separate from classical stochastic resonance reported in non-phasic neurons. The general features of our theory suggest that noise-enhanced codes in excitable systems with subthreshold negative feedback are a particularly rich framework to study.

  6. 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.

  7. Financial time series prediction using spiking neural networks.

    Science.gov (United States)

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  8. 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.

  9. Scaling of spiking and humping in keyhole welding

    Energy Technology Data Exchange (ETDEWEB)

    Wei, P S; Chuang, K C [Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan (China); DebRoy, T [Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802 (United States); Ku, J S, E-mail: pswei@mail.nsysu.edu.tw, E-mail: cielo.zhuang@gmail.com, E-mail: rtd1@psu.edu, E-mail: jsku@mail.nsysu.edu.tw [Institute of Materials Science and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan (China)

    2011-06-22

    Spiking, rippling and humping seriously reduce the strength of welds. The effects of beam focusing, volatile alloying element concentration and welding velocity on spiking, coarse rippling and humping in keyhole mode electron-beam welding are examined through scale analysis. Although these defects have been studied in the past, the mechanisms for their formation are not fully understood. This work relates the average amplitudes of spikes to fusion zone depth for the welding of Al 6061, SS 304 and carbon steel, and Al 5083. The scale analysis introduces welding and melting efficiencies and an appropriate power distribution to account for the focusing effects, and the energy which is reflected and escapes through the keyhole opening to the surroundings. The frequency of humping and spiking can also be predicted from the scale analysis. The analysis also reveals the interrelation between coarse rippling and humping. The data and the mechanistic findings reported in this study are useful for understanding and preventing spiking and humping during keyhole mode electron and laser beam welding.

  10. Neural Spike Train Synchronisation Indices: Definitions, Interpretations and Applications.

    Science.gov (United States)

    Halliday, D M; Rosenberg, J R

    2017-04-24

    A comparison of previously defined spike train syncrhonization indices is undertaken within a stochastic point process framework. The second order cumulant density (covariance density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% { 160% of the estimated value. On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a single population measure. The pooled coherence framework allows pooled time domain measures to be derived, application of this to the simulated data is illustrated. Data from the cortical neurone model is generated over a wide range of firing rates (1 - 250 spikes/sec). The pooled coherence framework correctly characterizes the sampling variability as not significant over this wide operating range. The broader applicability of this approach to multi electrode array data is briefly discussed.

  11. 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

  12. Preservative treatments for building components

    Science.gov (United States)

    Stan Lebow

    2007-01-01

    The wood species most commonly used in construction have little natural durability Thus, they are treated with preservatives when used in conditions that favor biodeterioration. The type of preservative used varies with the type of wood product, exposure condition, and specific agent of deterioration. This paper discusses the characteristics of several preservative...

  13. Paper-based archiving of biological samples from fish for detecting betanodavirus.

    Science.gov (United States)

    Navaneeth Krishnan, A; Bhuvaneswari, T; Ezhil Praveena, P; Jithendran, K P

    2016-07-01

    This study was carried out to evaluate the efficiency of the Flinders Technology Associates (FTA(®)) card (Whatman(®)) as a sampling device and storage platform for RNA from betanodavirus-infected biological samples (viz., larvae, broodstock, cell culture supernatants and rearing seawater spiked with infected materials). The study showed that FTA cards can be used to detect betanodaviruses by reverse transcription-polymerase chain reaction (RT-PCR). The diagnostic efficiency of RT-PCR from all sample types on FTA cards decreased after 21 days of storage at 4 °C, although the virus could be detected up to 28 days by nested RT-PCR. The FTA card protocol thus provides a supplementary method for quick and easy collection of samples, preservation of RNA on a dry storage basis, and detection of betanodavirus-infected fish.

  14. Preserving the Manhattan Project

    Science.gov (United States)

    Kelly, Cynthia

    2014-03-01

    When future generations look back on the 20th century, few events will rival the harnessing of nuclear energy as a turning point in world history, science and society. Yet, the Department of Energy has not always embraced its Manhattan Project origins. The presentation will focus on the progress made over the last 20 years to preserve the properties and first-hand accounts that for decades have been threatened with demolition and indifference. Since the mid-1950s, most remaining Manhattan Project properties at the Los Alamos National Laboratory had been abandoned. Among them was a cluster of wooden buildings called the ``V Site.'' This is where scientists assembled the ``Gadget,'' the world's first atomic device tested on July 16, 1945. Regardless of its significance, the ``V Site'' buildings like all the rest were slated for demolition. The Advisory Council on Historic Preservation (ACHP) toured the properties in November 1998. Most could not believe that the world's first atomic bomb was designed in such humble structures. The properties were declared to be ``monumental in their lack of monumentality.'' A Save America's Treasures grant for 700,000 was awarded to restore the properties. To raise the required matching funds, I left the Federal government and soon founded the Atomic Heritage Foundation. The presentation will trace the progress made over the last decade to generate interest and support nationwide to preserve the Manhattan Project heritage. Saving both the physical properties and first-hand accounts of the men and women have been a priority. Perhaps our most significant achievement may be legislation now under consideration by Congress to create a Manhattan Project National Historical Park. Seventy years later, the Manhattan Project is finally getting the recognition it deserves.

  15. Fertility Preservation in Girls

    Directory of Open Access Journals (Sweden)

    Jennia Michaeli

    2012-01-01

    Full Text Available Children that undergo treatment for cancer are at risk of suffering from subfertility or hormonal dysfunction due to the detrimental effects of radiotherapy and chemotherapeutic agents on the gonads. Cryopreservation of ovarian tissue prior to treatment offers the possibility of restoring gonadal function after resumption of therapy. Effective counseling and management of pediatric patients is crucial for preserving their future reproductive potential. The purpose of this article is to review recent literature and to revise recommendations we made in a 2007 article. Pediatric hemato-oncology, reproductive endocrinology, surgery, anesthesia and bioethics perspectives are discussed and integrated to propose guidelines for offering ovarian cryopreservation to premenarcheal girls with cancer.

  16. Radiation preservation of spices

    International Nuclear Information System (INIS)

    Badshah, A.; Tasnim, A.; Khan, M.; Sattar, A.; Khan, I.

    1989-01-01

    The use of gamma irradiation for preservation of red hot pepper has been explained in report, as it can kill the harmful organisms without altering the organolpetic properties. The sample were dried and reduced to pass through 20 mesh. The samples were irradiated at different dose levels of 0, 2.5, 5.0, 7.5 and 10.0 KGy and results have been shown after different time intervals. Radiation and packaging treatments resulted normaly no effect on the color of dry fruits. (A.B)

  17. Preserving Transactional Data

    OpenAIRE

    Sara Day Thomson

    2017-01-01

    This paper is an adaptation of a longer report commissioned by the UK Data Service. The longer report contributes to on-going support for the Big Data Network – a programme funded by the Economic and Social Research Council (ESRC). The longer report can be found at doi:10.7207/twr16-02. This paper discusses requirements for preserving transactional data and the accompanying challenges facing the companies and institutions who aim to re-use these data for analysis or research. It present...

  18. 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...

  19. Character recognition from trajectory by recurrent spiking neural networks.

    Science.gov (United States)

    Jiangrong Shen; Kang Lin; Yueming Wang; Gang Pan

    2017-07-01

    Spiking neural networks are biologically plausible and power-efficient on neuromorphic hardware, while recurrent neural networks have been proven to be efficient on time series data. However, how to use the recurrent property to improve the performance of spiking neural networks is still a problem. This paper proposes a recurrent spiking neural network for character recognition using trajectories. In the network, a new encoding method is designed, in which varying time ranges of input streams are used in different recurrent layers. This is able to improve the generalization ability of our model compared with general encoding methods. The experiments are conducted on four groups of the character data set from University of Edinburgh. The results show that our method can achieve a higher average recognition accuracy than existing methods.

  20. 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

  1. 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.

  2. Quantifying bamboo coral growth rate nonlinearity with the radiocarbon bomb spike: A new model for paleoceanographic chronology development

    Science.gov (United States)

    Frenkel, M. M.; LaVigne, M.; Miller, H. R.; Hill, T. M.; McNichol, A.; Gaylord, M. Lardie

    2017-07-01

    Bamboo corals, long-lived cold water gorgonin octocorals, offer unique paleoceanographic archives of the intermediate ocean. These Isididae corals are characterized by alternating gorgonin nodes and high Mg-calcite internodes, which synchronously extend radially. Bamboo coral calcite internodes have been utilized to obtain geochemical proxy data, however, growth rate uncertainty has made it difficult to construct precise chronologies for these corals. Previous studies have relied upon a single tie point from records of the anthropogenic Δ14C bomb spike preserved in the gorgonin nodes of live-collected corals to calculate a mean radial extension rate for the outer 50 years of skeletal growth. Bamboo coral chronologies are typically constructed by applying this mean extension rate to the entire coral record, assuming constant radial extension with coral age. In this study, we aim to test this underlying assumption by analyzing the organic nodes of six California margin bamboo corals at high enough resolution (bomb spike, including two tie points at 1957 and 1970, plus the coral collection date (2007.5) for four samples. Radial extension rates between tie points ranged from 10 to 204 μm/year, with a decrease in growth rate evident between the 1957-1970 and 1970-2007.5 periods for all four corals. A negative correlation between growth rate and coral radius (r =-0.7; p=0.04) was determined for multiple bamboo coral taxa and individuals from the California margin, demonstrating a decline in radial extension rate with specimen age and size. To provide a mechanistic basis for these observations, a simple mathematical model was developed based on the assumption of a constant increase in circular cross sectional area with time to quantify this decline in radial extension rate with coral size between chronological tie points. Applying the area-based model to our Δ14C bomb spike time series from individual corals improves chronology accuracy for all live-collected corals

  3. Mutual preservation of entanglement

    International Nuclear Information System (INIS)

    Veitia, Andrzej; Jing, Jun; Yu, Ting; Wong, Chee Wei

    2012-01-01

    We study a generalized double Jaynes–Cummings (JC) model where two entangled pairs of two-level atoms interact indirectly. We show that there exist initial states of the qubit system so that two entangled pairs are available at all times. In particular, the minimum entanglement in the pairs as a function of the initial state is studied. Finally, we extend our findings to a model consisting of multi-mode atom–cavity interactions. We use a non-Markovian quantum state diffusion (QSD) equation to obtain the steady-state density matrix for the qubits. We show that the multi-mode model also displays dynamical preservation of entanglement. -- Highlights: ► Entanglement dynamics is studied in a generalized double Jaynes–Cummings model. ► We show that for certain initial states, the atoms remain entangled at all times. ► We extend the results to the case of multi-mode atom–cavity interactions. ► The model suggest that indirect interaction may help to preserve entanglement.

  4. Spike and burst coding in thalamocortical relay cells.

    Directory of Open Access Journals (Sweden)

    Fleur Zeldenrust

    2018-02-01

    Full Text Available Mammalian thalamocortical relay (TCR neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices as well as in a validated three-compartment TCR model cell. The resulting membrane voltage traces and spike trains were analyzed by calculating the coherence and impedance. Reverse correlation techniques gave the Event-Triggered Average (ETA and the Event-Triggered Covariance (ETC. This demonstrated that the feature selectivity started relatively long before the events (up to 300 ms and showed a clear distinction between spikes (selective for fluctuations and bursts (selective for integration. The model cell was fine-tuned to mimic the frozen noise initiated spike and burst responses to within experimental accuracy, especially for the mixed mode regimes. The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. The model was then used to elucidate properties that could not be assessed experimentally, in particular the role of two important subthreshold voltage-dependent currents: the low threshold activated calcium current (IT and the cyclic nucleotide modulated h current (Ih. The ETAs of those currents and their underlying activation/inactivation states not only explained the state dependence of the firing regime but also the long-lasting concerted dynamic action of the two

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

    Science.gov (United States)

    Sanlı, Ceyda; Lambiotte, Renaud

    2015-01-01

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

  6. Sleep deprivation and spike-wave discharges in epileptic rats

    OpenAIRE

    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 increase in the number of spike-wave discharges was found during the first 4 hours of the deprivation period, whereas in the following deprivation hours epileptic activity returned to baseline values. I...

  7. 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.

  8. Spiking neuron devices consisting of single-flux-quantum circuits

    International Nuclear Information System (INIS)

    Hirose, Tetsuya; Asai, Tetsuya; Amemiya, Yoshihito

    2006-01-01

    Single-flux-quantum (SFQ) circuits can be used for making spiking neuron devices, which are useful elements for constructing intelligent, brain-like computers. The device we propose is based on the leaky integrate-and-fire neuron (IFN) model and uses a SFQ pulse as an action signal or a spike of neurons. The operation of the neuron device is confirmed by computer simulator. It can operate with a short delay of 100 ps or less and is the highest-speed neuron device ever reported

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

    Directory of Open Access Journals (Sweden)

    Claire Ramus

    2016-03-01

    Full Text Available 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 http://www.ebi.ac.uk/pride/archive/projects/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.

  10. SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

    Science.gov (United States)

    Jimenez-Romero, Cristian; Johnson, Jeffrey

    2017-01-01

    The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

  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. Preserving reptiles for research

    Science.gov (United States)

    Gotte, Steve W.; Jacobs, Jeremy F.; Zug, George R.; Dodd, C. Kenneth

    2016-01-01

    What are voucher specimens and why do we collect them? Voucher specimens are animals and/or their parts that are deposited in a research museum to document the occurrence of a taxon at a specific location in space and time (Pleijel et al., 2008; Reynolds and McDiarmid, 2012). For field biologists, vouchers are the repeatable element of a field study as they allow other biologists, now and in the future, to confirm the identity of species that were studied. The scientific importance of a voucher specimen or series of specimens is that other people are afforded the opportunity to examine the entire animal and confirm or correct identifications. A photographic record is somewhat useful for recording the occurrence of a species, but such records can be insufficient for reliable confirmation of specific identity. Even if a photo shows diagnostic characters of currently recognized taxa, it may not show characters that separate taxa that may be described in the future. Substantial cryptic biodiversity is being found in even relatively well-known herpetofaunas (Crawford et al., 2010), and specimens allow researchers to retroactively evaluate the true diversity in a study as understanding of taxonomy evolves. They enable biologists to study the systematic relationships of populations by quantifying variation in different traits. Specimens are also a source of biological data such as behaviour, ecology, epidemiology, and reproduction through examination of their anatomy, reproductive and digestive tracts, and parasites (Suarez and Tsutsui, 2004). Preserving reptiles as vouchers is not difficult, although doing it properly requires care, effort, and time. Poorly preserved vouchers can invalidate the results and conclusions of your study because of the inability to confirm the identity of your study animals. Good science requires repeatability of observations, and the absence of vouchers or poorly preserved ones prevents such confirmation. Due to space restrictions, we are

  13. Food preservation by irradiation

    International Nuclear Information System (INIS)

    Gottschalk, M.

    1978-01-01

    In November, 1977, an International Symposium on Food Preservation by Irradiation was held at Wageningen, the Netherlands. About 200 participants attended the Symposium which was organised by the International Atomic Energy Agency, the Food and Agriculture Organization of the United Nations and the World Health Organization; a reflection of the active interest which is being shown in food irradiation processing, particularly among developing countries. The 75 papers presented provided an excellent review of the current status of food irradiation on a wide range of different topics, and the Symposium also afforded the valuable opportunity for informal discussion among the participants and for developing personal contacts. A brief survey of the salient aspects discussed during the course of the meeting are reported on. (orig.) [de

  14. 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.

  15. 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...

  16. 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.

  17. Spike sorting based upon machine learning algorithms (SOMA).

    Science.gov (United States)

    Horton, P M; Nicol, A U; Kendrick, K M; Feng, J F

    2007-02-15

    We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

  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. Thermal spike analysis of highly charged ion tracks

    International Nuclear Information System (INIS)

    Karlušić, M.; Jakšić, M.

    2012-01-01

    The irradiation of material using swift heavy ion or highly charged ion causes excitation of the electron subsystem at nanometer scale along the ion trajectory. According to the thermal spike model, energy deposited into the electron subsystem leads to temperature increase due to electron–phonon coupling. If ion-induced excitation is sufficiently intensive, then melting of the material can occur, and permanent damage (i.e., ion track) can be formed upon rapid cooling. We present an extension of the analytical thermal spike model of Szenes for the analysis of surface ion track produced after the impact of highly charged ion. By applying the model to existing experimental data, more than 60% of the potential energy of the highly charged ion was shown to be retained in the material during the impact and transformed into the energy of the thermal spike. This value is much higher than 20–40% of the transferred energy into the thermal spike by swift heavy ion. Thresholds for formation of highly charged ion track in different materials show uniform behavior depending only on few material parameters.

  20. 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...

  1. 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.

  2. Sleep deprivation and spike-wave discharges in epileptic rats

    NARCIS (Netherlands)

    Drinkenburg, W.H.I.M.; Coenen, A.M.L.; Vossen, J.M.H.; Luijtelaar, E.L.J.M. van

    1995-01-01

    The effects of sleep deprivation were studied on the occurrence of spike-wave discharges in the electroencephalogram of rats of the epileptic WAG/Rij strain, a model for absence epilepsy. This was done before, during and after a period of 12 hours of near total sleep deprivation. A substantial

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

    Science.gov (United States)

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

    2017-03-01

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

  4. Spike Neural Models Part II: Abstract Neural Models

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2018-02-01

    Full Text Available 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 which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

  5. 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

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

    Science.gov (United States)

    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

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

  7. Dynamics of directional coupling underlying spike-wave discharges

    NARCIS (Netherlands)

    Sysoeva, M.V.; Luttjohann, A.K.; Luijtelaar, E.L.J.M. van; Sysoev, I.V.

    2016-01-01

    Purpose: Spike and wave discharges (SWDs), generated within cortico-thalamo-cortical networks, are the electroencephalographic biomarker of absence epilepsy. The current work aims to identify mechanisms of SWD initiation, maintenance and termination by the analyses of dynamics and directionality of

  8. Breathing, spiking and chaos in a laser with injected signal

    Energy Technology Data Exchange (ETDEWEB)

    Lugiato, L A; Narducci, L M

    1983-06-01

    The behavior of a laser driven by an injected cw field detuned from the operating laser frequency is considered. The analysis covers the entire range of incident power levels from zero to the injection locking threshold. In this domain, the output intensity exhibits regular and chaotic oscillations, a period doubling cascade in reverse order, envelope breathing and spiking.

  9. Inhibitory Synaptic Plasticity - Spike timing dependence and putative network function.

    Directory of Open Access Journals (Sweden)

    Tim P Vogels

    2013-07-01

    Full Text Available While the plasticity of excitatory synaptic connections in the brain has been widely studied, the plasticity of inhibitory connections is much less understood. Here, we present recent experimental and theoretical □ndings concerning the rules of spike timing-dependent inhibitory plasticity and their putative network function. This is a summary of a workshop at the COSYNE conference 2012.

  10. 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.

  11. 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.

  12. Fertility Preservation for Children Diagnosed with Cancer

    Medline Plus

    Full Text Available ... Provider Pocket Guides Provider Guides Fertility Preservation for Women Diagnosed with Cancer Fertility Preservation for Men Diagnosed ... Patient Pocket Guides Patient Guides Fertility Preservation for Women Diagnosed with Cancer Fertility Preservation for Men Diagnosed ...

  13. Fertility Preservation for Children Diagnosed with Cancer

    Medline Plus

    Full Text Available ... for Women Diagnosed with Cancer Fertility Preservation for Men Diagnosed with Cancer Fertility Preservation for Children Diagnosed ... for Women Diagnosed with Cancer Fertility Preservation for Men Diagnosed with Cancer Fertility Preservation for Children Diagnosed ...

  14. Inverse spiking filter based acquisition enhancement in software based global positioning system receiver

    Directory of Open Access Journals (Sweden)

    G. Arul Elango

    2015-01-01

    Full Text Available The lower visibility of the satellite in the acquisition stage of a GPS receiver under worst noisy situation leads to reacquisition of the data and thereby takes a longer time to obtain the first position fix. If the impulse noise affects the GPS signal, the conventional ways of acquiring the satellites do not guarantee to meet the minimum requirement of four satellites to find the user position. The performance of GPS receiver acquisition can be improved in the low SNR level using inverse spiking filtering technique. In the proposed method, the estimate of the desired GPS L1 signal corrupted by impulse noise (gn is obtained by the prediction error filter (hopt, which is the optimum inverse filter that reshapes the noisy signal (yn into a desired GPS signal (xn. In the proposed method, to detect the visible satellites under weak signal conditions the traditional differential coherent approach is combined with the inverse spiking filter method to increase the number of visible satellites and to avoid the reacquisition process. Montecarlo simulation is carried out to assess the performance of the proposed method for C/N0 of 20 dB-Hz and results indicate that the modified differential coherent method effectively excises the noise with 90% probability of detection. Subsequently tracking operation is also tested to confirm the acquisition performance by demodulating the navigation data successfully.

  15. 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.

  16. 2015 Site Environmental Report Fernald Preserve

    Energy Technology Data Exchange (ETDEWEB)

    Hertel, Bill [Navarro Research and Engineering, Oak Ridge, TN (United States); Hooten, Gwen [US Department of Energy, Washington, DC (United States)

    2016-05-01

    The Fernald Preserve 2015 Site Environmental Report provides stakeholders with the results from the Fernald, Ohio, Site’s environmental monitoring programs for 2015; a summary of the U.S. Department of Energy’s (DOE’s) activities conducted onsite; and a summary of the Fernald Preserve’s compliance with the various environmental regulations, compliance agreements, and DOE policies that govern site activities. This report has been prepared in accordance with the “Integrated Environmental Monitoring Plan,” which is Attachment D of the Comprehensive Legacy Management and Institutional Controls Plan (LMICP) (DOE 2016). Remediation of the Fernald Preserve has been successfully completed with the exception of the groundwater. During 2015, activities at the Fernald Preserve included: environmental monitoring activities related to direct radiation, groundwater, and surface water; ecological restoration monitoring and maintenance as well as inspections, care, and monitoring of the site and the OSDF to ensure that provisions of the LMICP are fully implemented; OSDF leak detection monitoring and collection, monitoring, and treatment of leachate from the OSDF; extraction, monitoring, and treatment of contaminated groundwater from the Great Miami Aquifer (Operable Unit 5); ongoing operation of the Fernald Preserve Visitors Center, associated outreach, and educational activities; and monitoring as specified in the site’s National Pollutant Discharge Elimination System (NPDES) permit. Environmental monitoring programs were developed to ensure that the remedy remains protective of the environment. The requirements of these programs are described in detail in the LMICP and reported in this Site Environmental Report.

  17. The importance of a Ni correction with ion counter in the double spike analysis of Fe isotope compositions using a 57Fe/58Fe double spike

    Science.gov (United States)

    Finlayson, V. A.; Konter, J. G.; Ma, L.

    2015-12-01

    We present a new method capable of measuring iron isotope ratios of igneous materials to high precision by multicollector inductively coupled plasma mass spectrometry (MC-ICP-MS) using a 57Fe-58Fe double spike. After sample purification, near-baseline signal levels of nickel are still present in the sample solution, acting as an isobaric interference on 58 amu. To correct for the interference, the minor 60Ni isotope is monitored and used to subtract a proportional 58Ni signal from the total 58 amu beam. The 60Ni signal is difficult to precisely measure on the Faraday detector due to Johnson noise occurring at similar magnitude. This noise-dominated signal is subtracted from the total 58 amu beam, and its error amplified during the double spike correction. Placing the 60Ni beam on an ion counter produces a more precise measurement, resulting in a near-threefold improvement in δ56Fe reproducibility, from ±0.145‰ when measured on Faraday to 0.052‰. Faraday detectors quantify the 60Ni signal poorly, and fail to discern the transient 20Ne40Ar interference visible on the ion counter, which is likely responsible for poor reproducibility. Another consideration is instrumental stability (defined herein as drift in peak center mass), which affects high-resolution analyses. Analyses experiencing large drift relative to bracketing standards often yield nonreplicating data. Based on this, we present a quantitative outlier detection method capable of detecting drift-affected data. After outlier rejection, long-term precision on individual runs of our secondary standard improves to ±0.046‰. Averaging 3-4 analyses further improves precision to 0.019‰, allowing distinction between ultramafic minerals.

  18. The fifth leaf and spike organs of barley (Hordeum vulgare L.) display different physiological and metabolic responses to drought stress.

    Science.gov (United States)

    Hein, Jordan A; Sherrard, Mark E; Manfredi, Kirk P; Abebe, Tilahun

    2016-11-09

    Photosynthetic organs of the cereal spike (ear) provide assimilate for grain filling, but their response to drought is poorly understood. In this study, we characterized the drought response of individual organs of the barley spike (awn, lemma, and palea) and compared them with a vegetative organ (fifth leaf). Understanding differences in physiological and metabolic responses between the leaf and spike organs during drought can help us develop high yielding cultivars for environments where terminal drought is prevalent. We exposed barley plants to drought by withholding water for 4 days at the grain filling stage and compared changes in: (1) relative water content (RWC), (2) osmotic potential (Ψ s ), (3) osmotic adjustment (OA), (4) gas exchange, and (5) metabolite content between organs. Drought reduced RWC and Ψ s in all four organs, but the decrease in RWC was greater and there was a smaller change in Ψ s in the fifth leaf than the spike organs. We detected evidence of OA in the awn, lemma, and palea, but not in the fifth leaf. Rates of gas exchange declined more rapidly in the fifth leaf than awn during drought. We identified 18 metabolites but, only ten metabolites accumulated significantly during drought in one or more organs. Among these, proline accumulated in all organs during drought while accumulation of the other metabolites varied between organs. This may suggest that each organ in the same plant uses a different set of osmolytes for drought resistance. Our results suggest that photosynthetic organs of the barley spike maintain higher water content, greater osmotic adjustment, and higher rates of gas exchange than the leaf during drought.

  19. Discovery of Consistent QTLs of Wheat Spike-Related Traits under Nitrogen Treatment at Different Development Stages

    Directory of Open Access Journals (Sweden)

    Zhiying Deng

    2017-12-01

    Full Text Available Spike-related traits such as spike length (Sl, fertile spikelet number (Fsn, sterile spikelet number (Ssn, grain number per spike (Gns, and thousand-kernel weight (Tkw are important factors influencing wheat yield. However, reliably stable markers that can be used for molecular breeding in different environments have not yet been identified. In this study, a double haploid (DH population was used for quantitative trait locus (QTL mapping of five spike-related traits under four different nitrogen (N supply dates in two locations and years. Seventy additive QTLs with phenotypic variation ranging from 4.12 to 34.74% and 10 major epistatic QTLs were identified. Eight important chromosomal regions on five chromosomes (1B, 2B, 2D, 5D, and 6A were found. Sixteen stable QTLs were detected for which N application had little effect. Among those stable QTLs, QSl.sdau-2D-1, and QSl.sdau-2D-2, with phenotypic variation explained (PVE of 10.4 and 24.2%, respectively, were flanked by markers Xwmc112 and Xcfd53 in the same order. The QTLs QSsn.sdau-2B-1, QFsn.sdau-2B-1, and QGns.sdau-2B, with PVE ranging from 4.37 to 28.43%, collocated in the Xwmc179-Xbarc373 marker interval. The consistent kernel wheat QTL (QTkw.sdau-6A on the long arm of chromosome 6A, flanked by SSR markers Xbarc1055 and Xwmc553, showed PVE of 5.87–15.18%. Among these stable QTLs, the two flanking markers Xwmc112 and Xcfd53 have been validated using different varieties and populations for selecting Sl. Therefore, these results will be of great value for marker-assisted selection (MAS in breeding programs and will accelerate the understanding of the genetic relationships among spike-related traits at the molecular level.

  20. Discovery of Consistent QTLs of Wheat Spike-Related Traits under Nitrogen Treatment at Different Development Stages.

    Science.gov (United States)

    Deng, Zhiying; Cui, Yong; Han, Qingdian; Fang, Wenqi; Li, Jifa; Tian, Jichun

    2017-01-01

    Spike-related traits such as spike length (Sl), fertile spikelet number (Fsn), sterile spikelet number (Ssn), grain number per spike (Gns), and thousand-kernel weight (Tkw) are important factors influencing wheat yield. However, reliably stable markers that can be used for molecular breeding in different environments have not yet been identified. In this study, a double haploid (DH) population was used for quantitative trait locus (QTL) mapping of five spike-related traits under four different nitrogen (N) supply dates in two locations and years. Seventy additive QTLs with phenotypic variation ranging from 4.12 to 34.74% and 10 major epistatic QTLs were identified. Eight important chromosomal regions on five chromosomes (1B, 2B, 2D, 5D, and 6A) were found. Sixteen stable QTLs were detected for which N application had little effect. Among those stable QTLs, QSl.sdau-2D-1 , and QSl.sdau-2D-2 , with phenotypic variation explained (PVE) of 10.4 and 24.2%, respectively, were flanked by markers Xwmc112 and Xcfd53 in the same order. The QTLs QSsn.sdau-2B-1, QFsn.sdau-2B-1 , and QGns.sdau-2B , with PVE ranging from 4.37 to 28.43%, collocated in the Xwmc179 - Xbarc373 marker interval. The consistent kernel wheat QTL ( QTkw.sdau-6A ) on the long arm of chromosome 6A, flanked by SSR markers Xbarc1055 and Xwmc553 , showed PVE of 5.87-15.18%. Among these stable QTLs, the two flanking markers Xwmc112 and Xcfd53 have been validated using different varieties and populations for selecting Sl. Therefore, these results will be of great value for marker-assisted selection (MAS) in breeding programs and will accelerate the understanding of the genetic relationships among spike-related traits at the molecular level.

  1. On the Use of 233U-236U Double-Spike for TIMS Measurements of Uranium Isotopes: A Simulation Study

    International Nuclear Information System (INIS)

    Williams, R W

    2004-01-01

    Synthetic ion beams with instantaneous and temporal characteristics appropriate to thermal ionization mass spectrometry (TIMS) were mathematically generated and analyzed to determine the effects of using a mixed 233 U- 236 U spike (double-spike) in the analysis of uranium isotopes. The instantaneous beam characteristics are the intensities (e.g., counts per second) modeled with a Poisson distribution plus a component of random noise that simulates the detection processes. Several beam intensity and mass fractionation vs. time functions were modeled to simulate a range of sample sizes and the commonly employed methods of data collection. These beam profiles were also generated with different noise levels, and signal-to-noise vs. analytical precision diagrams are presented. Modeling focused on natural uranium, where 238 U/ 235 U = 137.88, and on the ability of a given method to determine precisely and accurately small variations in this ratio. Practical limits on precision were determined to be 20-30 ppm, which is consistent with precision seen for other elements by state-of-the-art TIMS. The TIMS total evaporation method was compared directly with the double-spike method. While similar analytical precisions are obtained with either method, the double-spike method of correcting for analytical bias gives more accurate results. The results of a total evaporation analysis will deviate from true by more than the analytical precision if as little as 0.05% of the signal is not integrated, whereas the accuracy and precision of the double-spiked analyses are always linked

  2. Preserving Employee Privacy in Wellness.

    Science.gov (United States)

    Terry, Paul E

    2017-07-01

    The proposed "Preserving Employee Wellness Programs Act" states that the collection of information about the manifested disease or disorder of a family member shall not be considered an unlawful acquisition of genetic information. The bill recognizes employee privacy protections that are already in place and includes specific language relating to nondiscrimination based on illness. Why did legislation expressly intending to "preserve wellness programs" generate such antipathy about wellness among journalists? This article argues that those who are committed to preserving employee wellness must be equally committed to preserving employee privacy. Related to this, we should better parse between discussions and rules about commonplace health screenings versus much less common genetic testing.

  3. Assembly of spikes into coronavirus particles is mediated by the carboxy-terminal domain of the spike protein

    NARCIS (Netherlands)

    Godeke, G J; de Haan, Cornelis A M; Rossen, J W; Vennema, H; Rottier, P J

    The type I glycoprotein S of coronavirus, trimers of which constitute the typical viral spikes, is assembled into virions through noncovalent interactions with the M protein. Here we demonstrate that incorporation is mediated by the short carboxy-terminal segment comprising the transmembrane and

  4. Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons.

    Directory of Open Access Journals (Sweden)

    Kyriaki Sidiropoulou

    Full Text Available Proper functioning of working memory involves the expression of stimulus-selective persistent activity in pyramidal neurons of the prefrontal cortex (PFC, which refers to neural activity that persists for seconds beyond the end of the stimulus. The mechanisms which PFC pyramidal neurons use to discriminate between preferred vs. neutral inputs at the cellular level are largely unknown. Moreover, the presence of pyramidal cell subtypes with different firing patterns, such as regular spiking and intrinsic bursting, raises the question as to what their distinct role might be in persistent firing in the PFC. Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS and an intrinsic bursting (IB model neuron. We identify synaptic location within the basal dendrites as a feature of stimulus selectivity. Specifically, persistent activity-inducing stimuli consist of activated synapses that are located more distally from the soma compared to non-inducing stimuli, in both model cells. In addition, the action potential (AP latency and the first few inter-spike-intervals of the neuronal response can be used to reliably detect inducing vs. non-inducing inputs, suggesting a potential mechanism by which downstream neurons can rapidly decode the upcoming emergence of persistent activity. While the two model neurons did not differ in the coding features of persistent activity emergence, the properties of persistent activity, such as the firing pattern and the duration of temporally-restricted persistent activity were distinct. Collectively, our results pinpoint to specific features of the neuronal response to a given

  5. Extraction and characterization of essential discharge patterns from multisite recordings of spiking ongoing activity.

    Directory of Open Access Journals (Sweden)

    Riccardo Storchi

    Full Text Available Neural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns.Our technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences, whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C,S,R to evaluate the average pattern complexity, the structure of essential classes and their stability in time.We validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized a selection of ongoing activity recordings. By the S index we identified unstable, moderatly and strongly stable patterns while by the C and the R indices we evidenced their non-random structure. Our algorithm seems able to extract interesting and non-trivial spatial dynamics from multisource neuronal recordings of ongoing and potentially stimulated activity. Combined with time-frequency analysis of LFPs could provide a powerful multiscale approach linking population

  6. On the Universality and Non-Universality of Spiking Neural P Systems With Rules on Synapses.

    Science.gov (United States)

    Song, Tao; Xu, Jinbang; Pan, Linqiang

    2015-12-01

    Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In this work, this number is improved to 6. Specifically, we construct a Turing universal spiking neural P system with rules on synapses having 6 neurons, which can generate any set of Turing computable natural numbers. As well, it is obtained that spiking neural P system with rules on synapses having less than two neurons are not Turing universal: i) such systems having one neuron can characterize the family of finite sets of natural numbers; ii) the family of sets of numbers generated by the systems having two neurons is included in the family of semi-linear sets of natural numbers.

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

    African Journals Online (AJOL)

    African Journal of Biotechnology ... Five wheat genotypes were crossed in complete diallel fashion for gene ... by pursuing pedigree method while heterosis can be exploited for spike length, grain weight per spike and grain yield per plant.

  8. Nuclear knowledge preservation

    International Nuclear Information System (INIS)

    Bettencourt, Marcia Pires da Luz

    2009-01-01

    The nuclear technology has encouraged the world development and brought a number of benefits to society. These benefits occurred in important social sectors such as Agriculture, Industry, Health Sciences, Environmental Sciences and the production of energy. The research in the nuclear area is justified, accordingly, as an important factor for science development, technology and innovation. Despite the importance of nuclear energy, there is a collapse in the generation, transmission and sharing of nuclear knowledge. The threat of regression in this area is evidenced by the difficulty of generating new knowledge and practices regarding the maintenance of some critical areas. This project focuses its attention on studying, specifically, the lack of young engineers and technical professionals to replace the older, considered this, an alarming situation. Therefore, it is necessary to identify and record the key skills of experienced workers, through a set of tools to elicitation (capture) this knowledge, as expertise is mainly with people, and is lost when they leave the organization. Against, the Knowledge Management provides methodologies for the process of stimulating the creation, collection and knowledge dissemination process, in order to achieve strategic objectives. This study aims to contribute to the building of a model for the Brazilian nuclear knowledge preservation and, therefore, contributes to the maintenance and innovation of activities in this area. (author)

  9. User Experience and Heritage Preservation

    Science.gov (United States)

    Orfield, Steven J.; Chapman, J. Wesley; Davis, Nathan

    2011-01-01

    In considering the heritage preservation of higher education campus buildings, much of the attention gravitates toward issues of selection, cost, accuracy, and value, but the model for most preservation projects does not have a clear method of achieving the best solutions for meeting these targets. Instead, it simply relies on the design team and…

  10. 2015 Site Environmental Report Fernald Preserve

    International Nuclear Information System (INIS)

    Hertel, Bill; Hooten, Gwen

    2016-01-01

    The Fernald Preserve 2015 Site Environmental Report provides stakeholders with the results from the Fernald, Ohio, Site's environmental monitoring programs for 2015; a summary of the U.S. Department of Energy's (DOE's) activities conducted onsite; and a summary of the Fernald Preserve's compliance with the various environmental regulations, compliance agreements, and DOE policies that govern site activities. This report has been prepared in accordance with the ''Integrated Environmental Monitoring Plan,'' which is Attachment D of the Comprehensive Legacy Management and Institutional Controls Plan (LMICP) (DOE 2016). Remediation of the Fernald Preserve has been successfully completed with the exception of the groundwater. During 2015, activities at the Fernald Preserve included: environmental monitoring activities related to direct radiation, groundwater, and surface water; ecological restoration monitoring and maintenance as well as inspections, care, and monitoring of the site and the OSDF to ensure that provisions of the LMICP are fully implemented; OSDF leak detection monitoring and collection, monitoring, and treatment of leachate from the OSDF; extraction, monitoring, and treatment of contaminated groundwater from the Great Miami Aquifer (Operable Unit 5); ongoing operation of the Fernald Preserve Visitors Center, associated outreach, and educational activities; and monitoring as specified in the site's National Pollutant Discharge Elimination System (NPDES) permit. Environmental monitoring programs were developed to ensure that the remedy remains protective of the environment. The requirements of these programs are described in detail in the LMICP and reported in this Site Environmental Report.

  11. Measures of spike train synchrony for data with multiple time scales

    NARCIS (Netherlands)

    Satuvuori, Eero; Mulansky, Mario; Bozanic, Nebojsa; Malvestio, Irene; Zeldenrust, Fleur; Lenk, Kerstin; Kreuz, Thomas

    2017-01-01

    Background Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by

  12. Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

    NARCIS (Netherlands)

    D. Zambrano (Davide); S.M. Bohte (Sander)

    2016-01-01

    textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

  13. Contact dermatitis caused by preservatives.

    Science.gov (United States)

    Yim, Elizabeth; Baquerizo Nole, Katherine L; Tosti, Antonella

    2014-01-01

    Preservatives are biocidal chemicals added to food, cosmetics, and industrial products to prevent the growth of microorganisms. They are usually nontoxic and inexpensive and have a long shelf life. Unfortunately, they commonly cause contact dermatitis. This article reviews the most important classes of preservatives physicians are most likely to encounter in their daily practice, specifically isothiazolinones, formaldehyde and formaldehyde-releasers, iodopropynyl butylcarbamate, methyldibromoglutaronitrile, and parabens. For each preservative mentioned, the prevalence of sensitization, clinical presentation of contact dermatitis, patch testing concentrations, cross reactions, and related legislation will be discussed. Mandatory labeling of preservatives is required in some countries, but not required in others. Until policies are made, physicians and patients must be proactive in identifying potential sensitizers and removing their use. We hope that this article will serve as a guide for policy makers in creating legislation and future regulations on the use and concentration of certain preservatives in cosmetics and industrial products.

  14. Spikes matter for phase-locked bursting in inhibitory neurons

    Science.gov (United States)

    Jalil, Sajiya; Belykh, Igor; Shilnikov, Andrey

    2012-03-01

    We show that inhibitory networks composed of two endogenously bursting neurons can robustly display several coexistent phase-locked states in addition to stable antiphase and in-phase bursting. This work complements and enhances our recent result [Jalil, Belykh, and Shilnikov, Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.81.045201 81, 045201(R) (2010)] that fast reciprocal inhibition can synchronize bursting neurons due to spike interactions. We reveal the role of spikes in generating multiple phase-locked states and demonstrate that this multistability is generic by analyzing diverse models of bursting networks with various fast inhibitory synapses; the individual cell models include the reduced leech heart interneuron, the Sherman model for pancreatic beta cells, and the Purkinje neuron model.

  15. Reflex reading epilepsy: effect of linguistic characteristics on spike frequency.

    Science.gov (United States)

    Safi, Dima; Lassonde, Maryse; Nguyen, Dang Khoa; Denault, Carole; Macoir, Joël; Rouleau, Isabelle; Béland, Renée

    2011-04-01

    Reading epilepsy is a rare reflex epilepsy in which seizures are provoked by reading. Several cases have been described in the literature, but the pathophysiological processes vary widely and remain unclear. We describe a 42-year-old male patient with reading epilepsy evaluated using clinical assessments and continuous video/EEG recordings. We administered verbal, nonverbal, and reading tasks to determine factors precipitating seizures. Linguistic characteristics of the words were manipulated. Results indicated that reading-induced seizures were significantly more numerous than those observed during verbal and nonverbal tasks. In reading tasks, spike frequency significantly increased with involvement of the phonological reading route. Spikes were recorded predominantly in left parasagittal regions. Future cerebral imaging studies will enable us to visualize the spatial localization and temporal course of reading-induced seizures and brain activity involved in reading. A better understanding of reading epilepsy is crucial for reading rehabilitation in these patients. Copyright © 2011 Elsevier Inc. All rights reserved.

  16. Negotiating Multicollinearity with Spike-and-Slab Priors.

    Science.gov (United States)

    Ročková, Veronika; George, Edward I

    2014-08-01

    In multiple regression under the normal linear model, the presence of multicollinearity is well known to lead to unreliable and unstable maximum likelihood estimates. This can be particularly troublesome for the problem of variable selection where it becomes more difficult to distinguish between subset models. Here we show how adding a spike-and-slab prior mitigates this difficulty by filtering the likelihood surface into a posterior distribution that allocates the relevant likelihood information to each of the subset model modes. For identification of promising high posterior models in this setting, we consider three EM algorithms, the fast closed form EMVS version of Rockova and George (2014) and two new versions designed for variants of the spike-and-slab formulation. For a multimodal posterior under multicollinearity, we compare the regions of convergence of these three algorithms. Deterministic annealing versions of the EMVS algorithm are seen to substantially mitigate this multimodality. A single simple running example is used for illustration throughout.

  17. Adrenalectomy eliminates the extinction spike in autoshaping with rats.

    Science.gov (United States)

    Thomas, B L; Papini, M R

    2001-03-01

    Experiment 1, using rats, investigated the effect of adrenalectomy (ADX) on the invigoration of lever-contact performance that occurs in the autoshaping situation after a shift from acquisition to extinction (called the extinction spike). Groups of rats with ADX or sham operations were trained under spaced and massed conditions [average intertrial intervals (ITI) of either 15 or 90 s] for 10 sessions and then shifted to extinction. ADX did not affect acquisition training but it eliminated the extinction spike. Plasma corticosterone levels during acquisition were shown in Experiment 2 to be similar in rats trained under spaced or massed conditions. Adrenal participation in the emotional arousal induced by conditions of surprising nonreward (e.g., extinction) is discussed.

  18. Past, present and future of spike sorting techniques.

    Science.gov (United States)

    Rey, Hernan Gonzalo; Pedreira, Carlos; Quian Quiroga, Rodrigo

    2015-10-01

    Spike sorting is a crucial step to extract information from extracellular recordings. With new recording opportunities provided by the development of new electrodes that allow monitoring hundreds of neurons simultaneously, the scenario for the new generation of algorithms is both exciting and challenging. However, this will require a new approach to the problem and the development of a common reference framework to quickly assess the performance of new algorithms. In this work, we review the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms. We conclude by proposing a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  19. Note on the coefficient of variations of neuronal spike trains.

    Science.gov (United States)

    Lengler, Johannes; Steger, Angelika

    2017-08-01

    It is known that many neurons in the brain show spike trains with a coefficient of variation (CV) of the interspike times of approximately 1, thus resembling the properties of Poisson spike trains. Computational studies have been able to reproduce this phenomenon. However, the underlying models were too complex to be examined analytically. In this paper, we offer a simple model that shows the same effect but is accessible to an analytic treatment. The model is a random walk model with a reflecting barrier; we give explicit formulas for the CV in the regime of excess inhibition. We also analyze the effect of probabilistic synapses in our model and show that it resembles previous findings that were obtained by simulation.

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

    Directory of Open Access Journals (Sweden)

    E. D. Belousova

    2012-01-01

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

  1. Supervised learning in spiking neural networks with FORCE training.

    Science.gov (United States)

    Nicola, Wilten; Clopath, Claudia

    2017-12-20

    Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.

  2. The Ripple Pond: Enabling Spiking Networks to See

    Directory of Open Access Journals (Sweden)

    Saeed eAfshar

    2013-11-01

    Full Text Available We present the biologically inspired Ripple Pond Network (RPN, a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially capable of delivering end-to-end high-speed, low-power and low-resolution recognition for mobile and autonomous applications where slow, highly sophisticated and power hungry signal processing solutions are ineffective. Key aspects in the proposed approach include utilising the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable temporal patterns and the use of asynchronous frames for information binding.

  3. The ripple pond: enabling spiking networks to see.

    Science.gov (United States)

    Afshar, Saeed; Cohen, Gregory K; Wang, Runchun M; Van Schaik, André; Tapson, Jonathan; Lehmann, Torsten; Hamilton, Tara J

    2013-01-01

    We present the biologically inspired Ripple Pond Network (RPN), a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns (TP) suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially capable of delivering end-to-end high-speed, low-power and low-resolution recognition for mobile and autonomous applications where slow, highly sophisticated and power hungry signal processing solutions are ineffective. Key aspects in the proposed approach include utilizing the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable TP and the use of asynchronous frames for information binding.

  4. Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware

    Directory of Open Access Journals (Sweden)

    Andreas Stöckel

    2017-08-01

    Full Text Available Large-scale neuromorphic hardware platforms, specialized computer systems for energy efficient simulation of spiking neural networks, are being developed around the world, for example as part of the European Human Brain Project (HBP. Due to conceptual differences, a universal performance analysis of these systems in terms of runtime, accuracy and energy efficiency is non-trivial, yet indispensable for further hard- and software development. In this paper we describe a scalable benchmark based on a spiking neural network implementation of the binary neural associative memory. We treat neuromorphic hardware and software simulators as black-boxes and execute exactly the same network description across all devices. Experiments on the HBP platforms under varying configurations of the associative memory show that the presented method allows to test the quality of the neuron model implementation, and to explain significant deviations from the expected reference output.

  5. A Theory of Material Spike Formation in Flow Separation

    Science.gov (United States)

    Serra, Mattia; Haller, George

    2017-11-01

    We develop a frame-invariant theory of material spike formation during flow separation over a no-slip boundary in two-dimensional flows with arbitrary time dependence. This theory identifies both fixed and moving separation, is effective also over short-time intervals, and admits a rigorous instantaneous limit. Our theory is based on topological properties of material lines, combining objectively stretching- and rotation-based kinematic quantities. The separation profile identified here serves as the theoretical backbone for the material spike from its birth to its fully developed shape, and remains hidden to existing approaches. Finally, our theory can be used to rigorously explain the perception of off-wall separation in unsteady flows, and more importantly, provide the conditions under which such a perception is justified. We illustrate our results in several examples including steady, time-periodic and unsteady analytic velocity fields with flat and curved boundaries, and an experimental dataset.

  6. Planning Annuaulised hours when spike in demand exists

    Directory of Open Access Journals (Sweden)

    MR Sureshkumar

    2012-04-01

    Full Text Available Manpower planning using annualised hours is an effective tool where seasonal demand for staff in industry exists. In annualised hours (AH workers are contracted to work for a certain number of hours per year. The workers are associated with relative efficiency for different types of tasks. This paper proposes a Mixed Integer linear Programming (MILP model to solve an annualised working hours planning problem when spike in demand exists. The holiday weeks for the workers are considered as partially individualised. If a worker has been assigned with more than one type of working week in a week, this will be compensated with one or more holiday week. The performance of the model is demonstrated with an example. It can be seen that this type of modelling helps to meet the spikes in demand with less capacity shortage compared with one working week in a week.

  7. Asymptotics of empirical eigenstructure for high dimensional spiked covariance.

    Science.gov (United States)

    Wang, Weichen; Fan, Jianqing

    2017-06-01

    We derive the asymptotic distributions of the spiked eigenvalues and eigenvectors under a generalized and unified asymptotic regime, which takes into account the magnitude of spiked eigenvalues, sample size, and dimensionality. This regime allows high dimensionality and diverging eigenvalues and provides new insights into the roles that the leading eigenvalues, sample size, and dimensionality play in principal component analysis. Our results are a natural extension of those in Paul (2007) to a more general setting and solve the rates of convergence problems in Shen et al. (2013). They also reveal the biases of estimating leading eigenvalues and eigenvectors by using principal component analysis, and lead to a new covariance estimator for the approximate factor model, called shrinkage principal orthogonal complement thresholding (S-POET), that corrects the biases. Our results are successfully applied to outstanding problems in estimation of risks of large portfolios and false discovery proportions for dependent test statistics and are illustrated by simulation studies.

  8. 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.

  9. Archeomagnetic Intensity Spikes: Global or Regional Geomagnetic Field Features?

    Directory of Open Access Journals (Sweden)

    Monika Korte

    2018-03-01

    Full Text Available Variations of the geomagnetic field prior to direct observations are inferred from archeo- and paleomagnetic experiments. Seemingly unusual variations not seen in the present-day and historical field are of particular interest to constrain the full range of core dynamics. Recently, archeomagnetic intensity spikes, characterized by very high field values that appear to be associated with rapid secular variation rates, have been reported from several parts of the world. They were first noted in data from the Levant at around 900 BCE. A recent re-assessment of previous and new Levantine data, involving a rigorous quality assessment, interprets the observations as an extreme local geomagnetic high with at least two intensity spikes between the 11th and 8th centuries BCE. Subsequent reports of similar features from Asia, the Canary Islands and Texas raise the question of whether such features might be common occurrences, or whether they might even be part of a global magnetic field feature. Here we use spherical harmonic modeling to test two hypotheses: firstly, whether the Levantine and other potential spikes might be associated with higher dipole field intensity than shown by existing global field models around 1,000 BCE, and secondly, whether the observations from different parts of the world are compatible with a westward drifting intense flux patch. Our results suggest that the spikes originate from intense flux patches growing and decaying mostly in situ, combined with stronger and more variable dipole moment than shown by previous global field models. Axial dipole variations no more than 60% higher than observed in the present field, probably within the range of normal geodynamo behavior, seem sufficient to explain the observations.

  10. Discriminating Sea Spikes in Incoherent Radar Measurements of Sea Clutter

    Science.gov (United States)

    2008-03-01

    het detecteren echter niet te verwachten dat bet gebruik van sea spikes te onderzoeken. Een van deze modellen zal leiden tot een Auteur (s) dergelijk...report I TNO-DV 2008 A067 6/33 Abbreviations CFAR Constant False-Alarm Rate CST Composite Surface Theory FFT Fast Fourier Transform PDF Probability Density...described by the composite surface theory (CST). This theory describes the sea surface as small Bragg-resonant capillary waves riding on top of

  11. Simulating large-scale spiking neuronal networks with NEST

    OpenAIRE

    Schücker, Jannis; Eppler, Jochen Martin

    2014-01-01

    The Neural Simulation Tool NEST [1, www.nest-simulator.org] is the simulator for spiking neural networkmodels of the HBP that focuses on the dynamics, size and structure of neural systems rather than on theexact morphology of individual neurons. Its simulation kernel is written in C++ and it runs on computinghardware ranging from simple laptops to clusters and supercomputers with thousands of processor cores.The development of NEST is coordinated by the NEST Initiative [www.nest-initiative.or...

  12. Spectrofluorimetric determination of aliskiren in tablets and spiked human plasma through derivatization with dansyl chloride.

    Science.gov (United States)

    Aydoğmuş, Zeynep; Sarı, Ferhat; Ulu, Sevgi Tatar

    2012-03-01

    A simple and sensitive method has been developed and validated for the determination of aliskiren (ALS) in its dosage forms and spiked plasma. The method was based on the reaction of the drug with dansyl chloride in the presence of bicarbonate solution of pH 10.5 to give a highly fluorescent derivative which was measured at 501 nm with excitition at 378 nm in dichloromethane. Different experimental parameters affecting the development of the method and stability were carefully studied and optimized. The calibration curves were linear over the concentration ranges of 100-700 and 50-150 ng/mL for standard solution and plasma, respectively. The limits of detection were 27.52 ng/mL in standard solution, 4.91 ng/mL in plasma. The developed method was successfully applied to the analysis the drug in the commercial tablets and spiked plasma samples. The mean recovery of ALS from tablets and plasma was 100.10 and 97.81%, respectively. A proposal of the reaction pathway was presented.

  13. Poisson-Like Spiking in Circuits with Probabilistic Synapses

    Science.gov (United States)

    Moreno-Bote, Rubén

    2014-01-01

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

  14. Emergent properties of interacting populations of spiking neurons

    Directory of Open Access Journals (Sweden)

    Stefano eCardanobile

    2011-12-01

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

  15. Emergent properties of interacting populations of spiking neurons.

    Science.gov (United States)

    Cardanobile, Stefano; Rotter, Stefan

    2011-01-01

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

  16. 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.

  17. Efficient computation in networks of spiking neurons: simulations and theory

    International Nuclear Information System (INIS)

    Natschlaeger, T.

    1999-01-01

    One of the most prominent features of biological neural systems is that individual neurons communicate via short electrical pulses, the so called action potentials or spikes. In this thesis we investigate possible mechanisms which can in principle explain how complex computations in spiking neural networks (SNN) can be performed very fast, i.e. within a few 10 milliseconds. Some of these models are based on the assumption that relevant information is encoded by the timing of individual spikes (temporal coding). We will also discuss a model which is based on a population code and still is able to perform fast complex computations. In their natural environment biological neural systems have to process signals with a rich temporal structure. Hence it is an interesting question how neural systems process time series. In this context we explore possible links between biophysical characteristics of single neurons (refractory behavior, connectivity, time course of postsynaptic potentials) and synapses (unreliability, dynamics) on the one hand and possible computations on times series on the other hand. Furthermore we describe a general model of computation that exploits dynamic synapses. This model provides a general framework for understanding how neural systems process time-varying signals. (author)

  18. 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

  19. Spike: Artificial intelligence scheduling for Hubble space telescope

    Science.gov (United States)

    Johnston, Mark; Miller, Glenn; Sponsler, Jeff; Vick, Shon; Jackson, Robert

    1990-01-01

    Efficient utilization of spacecraft resources is essential, but the accompanying scheduling problems are often computationally intractable and are difficult to approximate because of the presence of numerous interacting constraints. Artificial intelligence techniques were applied to the scheduling of the NASA/ESA Hubble Space Telescope (HST). This presents a particularly challenging problem since a yearlong observing program can contain some tens of thousands of exposures which are subject to a large number of scientific, operational, spacecraft, and environmental constraints. New techniques were developed for machine reasoning about scheduling constraints and goals, especially in cases where uncertainty is an important scheduling consideration and where resolving conflicts among conflicting preferences is essential. These technique were utilized in a set of workstation based scheduling tools (Spike) for HST. Graphical displays of activities, constraints, and schedules are an important feature of the system. High level scheduling strategies using both rule based and neural network approaches were developed. While the specific constraints implemented are those most relevant to HST, the framework developed is far more general and could easily handle other kinds of scheduling problems. The concept and implementation of the Spike system are described along with some experiments in adapting Spike to other spacecraft scheduling domains.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Karim El-Laithy

    2011-01-01

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

  2. How adaptation shapes spike rate oscillations in recurrent neuronal networks

    Directory of Open Access Journals (Sweden)

    Moritz eAugustin

    2013-02-01

    Full Text Available Neural mass signals from in-vivo recordings often show oscillations with frequencies ranging from <1 Hz to 100 Hz. Fast rhythmic activity in the beta and gamma range can be generated by network based mechanisms such as recurrent synaptic excitation-inhibition loops. Slower oscillations might instead depend on neuronal adaptation currents whose timescales range from tens of milliseconds to seconds. Here we investigate how the dynamics of such adaptation currents contribute to spike rate oscillations and resonance properties in recurrent networks of excitatory and inhibitory neurons. Based on a network of sparsely coupled spiking model neurons with two types of adaptation current and conductance based synapses with heterogeneous strengths and delays we use a mean-field approach to analyze oscillatory network activity. For constant external input, we find that spike-triggered adaptation currents provide a mechanism to generate slow oscillations over a wide range of adaptation timescales as long as recurrent synaptic excitation is sufficiently strong. Faster rhythms occur when recurrent inhibition is slower than excitation and oscillation frequency increases with the strength of inhibition. Adaptation facilitates such network based oscillations for fast synaptic inhibition and leads to decreased frequencies. For oscillatory external input, adaptation currents amplify a narrow band of frequencies and cause phase advances for low frequencies in addition to phase delays at higher frequencies. Our results therefore identify the different key roles of neuronal adaptation dynamics for rhythmogenesis and selective signal propagation in recurrent networks.

  3. 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.

  4. Linking structure and activity in nonlinear spiking networks.

    Science.gov (United States)

    Ocker, Gabriel Koch; Josić, Krešimir; Shea-Brown, Eric; Buice, Michael A

    2017-06-01

    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.

  5. Stochastic models for spike trains of single neurons

    CERN Document Server

    Sampath, G

    1977-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Fernando Henrique de Sousa

    2017-03-01

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

  7. Economic impact on the Florida economy of energy price spikes

    International Nuclear Information System (INIS)

    Mory, J.F.

    1992-01-01

    A substantial disturbance in oil supplies is likely to generate a large price upsurge and a downturn in the level of economic activity. Each of these two effects diminishes demand by a certain amount. The specific price surge required to reduce demand to the lower level of supply can be calculated with an oil demand function and with empirical estimations of the association between price spikes and declines in economic activity. The first section presents an energy demand model for Florida, which provides the price and income elasticities needed. The second section includes theoretical explanations and empirical estimations of the relationship between price spikes and recessions. Based on historical evidence, it seems that Florida's and the nation's economic systems are very sensitive to oil price surges. As price spikes appear damaging to the economy, it could be expected that reductions in the price of oil are beneficial to the system. That is likely to be the case in the long run, but no empirical evidence of favorable short-term effects of oil price decreases was found. Several possible explanations and theoretical reasons are offered to explain this lack of association. The final section presents estimates of the effect of oil disruptions upon specific industries in Florida and the nation

  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. Synchrony detection and amplification by silicon neurons with STDP synapses.

    Science.gov (United States)

    Bofill-i-petit, Adria; Murray, Alan F

    2004-09-01

    Spike-timing dependent synaptic plasticity (STDP) is a form of plasticity driven by precise spike-timing differences between presynaptic and postsynaptic spikes. Thus, the learning rules underlying STDP are suitable for learning neuronal temporal phenomena such as spike-timing synchrony. It is well known that weight-independent STDP creates unstable learning processes resulting in balanced bimodal weight distributions. In this paper, we present a neuromorphic analog very large scale integration (VLSI) circuit that contains a feedforward network of silicon neurons with STDP synapses. The learning rule implemented can be tuned to have a moderate level of weight dependence. This helps stabilise the learning process and still generates binary weight distributions. From on-chip learning experiments we show that the chip can detect and amplify hierarchical spike-timing synchrony structures embedded in noisy spike trains. The weight distributions of the network emerging from learning are bimodal.

  10. Food preservation by irradiation

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1978-02-15

    As shortages of food and energy still continue to constitute the major threats to the well-being of the human race, all actions aiming at overcoming these problems must be assigned vital importance. Of the two complementary ways of solving the food problem (i.e., increasing the production of food and decreasing the spoilage of food) a novel method designed to contribute to the latter purpose has been discussed at this symposium hosted by The Netherlands and held under the aegis of the Food and Agriculture Organization of the United Nations, the International Atomic Energy Agency and the World Health Organization. Progress made since the last symposium of this kind (Bombay, India, 1972) was reviewed from the technological, economic and wholesomeness points of view by participants from 39 countries (60% of the latter were of the developing world). From the reports presented on the use of radiations to control physiological changes in plants, feasibility of radiation preservation of potatoes, onions, garlic, as well as of some tropical and subtropical fruits (mangoes, papayas, litchis and avocado) was confirmed. For potatoes, onions and mangoes, optimal conditions of treatment and storage were established on a larger scale, combined with sizeable consumer trials. Combinations of ionizing radiation with chemicals (salycilic acid, for potatoes), and physical agents (ultraviolet rays, for papayas) have been reported to be successful against the incidence of rot. A considerable number of papers dealt with the control of microbiological spoilage of foods. Work since 1972 has shown that radurization of fruits and vegetables (bananas, mangoes, dried dates, endive, chickory, onions, soup-greens), meat, poultry, marine products (mackerel, cod and plaice fillets, shrimps), decontamination of food ingredients and food technology aids (enzyme preparations, proteins, starch, spices), radappertization of meat and animal feedstuffs as well as combination treatments with salt, heat

  11. Food preservation by irradiation

    International Nuclear Information System (INIS)

    1978-01-01

    As shortages of food and energy still continue to constitute the major threats to the well-being of the human race, all actions aiming at overcoming these problems must be assigned vital importance. Of the two complementary ways of solving the food problem (i.e., increasing the production of food and decreasing the spoilage of food) a novel method designed to contribute to the latter purpose has been discussed at this symposium hosted by The Netherlands and held under the aegis of the Food and Agriculture Organization of the United Nations, the International Atomic Energy Agency and the World Health Organization. Progress made since the last symposium of this kind (Bombay, India, 1972) was reviewed from the technological, economic and wholesomeness points of view by participants from 39 countries (60% of the latter were of the developing world). From the reports presented on the use of radiations to control physiological changes in plants, feasibility of radiation preservation of potatoes, onions, garlic, as well as of some tropical and subtropical fruits (mangoes, papayas, litchis and avocado) was confirmed. For potatoes, onions and mangoes, optimal conditions of treatment and storage were established on a larger scale, combined with sizeable consumer trials. Combinations of ionizing radiation with chemicals (salycilic acid, for potatoes), and physical agents (ultraviolet rays, for papayas) have been reported to be successful against the incidence of rot. A considerable number of papers dealt with the control of microbiological spoilage of foods. Work since 1972 has shown that radurization of fruits and vegetables (bananas, mangoes, dried dates, endive, chickory, onions, soup-greens), meat, poultry, marine products (mackerel, cod and plaice fillets, shrimps), decontamination of food ingredients and food technology aids (enzyme preparations, proteins, starch, spices), radappertization of meat and animal feedstuffs as well as combination treatments with salt, heat

  12. Correlations decrease with propagation of spiking activity in the mouse barrel cortex

    Directory of Open Access Journals (Sweden)

    Gayathri Nattar Ranganathan

    2011-05-01

    Full Text Available Propagation of suprathreshold spiking activity through neuronal populations is important for the function of the central nervous system. Neural correlations have an impact on cortical function particularly on the signaling of information and propagation of spiking activity. Therefore we measured the change in correlations as suprathreshold spiking activity propagated between recurrent neuronal networks of the mammalian cerebral cortex. Using optical methods we recorded spiking activity from large samples of neurons from two neural populations simultaneously. The results indicate that correlations decreased as spiking activity propagated from layer 4 to layer 2/3 in the rodent barrel cortex.

  13. Fast convergence of spike sequences to periodic patterns in recurrent networks

    International Nuclear Information System (INIS)

    Jin, Dezhe Z.

    2002-01-01

    The dynamical attractors are thought to underlie many biological functions of recurrent neural networks. Here we show that stable periodic spike sequences with precise timings are the attractors of the spiking dynamics of recurrent neural networks with global inhibition. Almost all spike sequences converge within a finite number of transient spikes to these attractors. The convergence is fast, especially when the global inhibition is strong. These results support the possibility that precise spatiotemporal sequences of spikes are useful for information encoding and processing in biological neural networks

  14. Contamination versus preservation of cosmetics

    DEFF Research Database (Denmark)

    Lundov, Michael Dyrgaard; Moesby, Lise; Zachariae, Claus

    2009-01-01

    Cosmetics with high water content are at a risk of being contaminated by micro-organisms that can alter the composition of the product or pose a health risk to the consumer. Pathogenic micro-organisms such as Staphylococcus aureus and Pseudomonas aeruginosa are frequently found in contaminated...... cosmetics. In order to avoid contamination of cosmetics, the manufacturers add preservatives to their products. In the EU and the USA, cosmetics are under legislation and all preservatives must be safety evaluated by committees. There are several different preservatives available but the cosmetic market...

  15. Transient reduction in theta power caused by interictal spikes in human temporal lobe epilepsy.

    Science.gov (United States)

    Manling Ge; Jundan Guo; Yangyang Xing; Zhiguo Feng; Weide Lu; Xinxin Ma; Yuehua Geng; Xin Zhang

    2017-07-01

    The inhibitory impacts of spikes on LFP theta rhythms(4-8Hz) are investigated around sporadic spikes(SSs) based on intracerebral EEG of 4 REM sleep patients with temporal lobe epilepsy(TLE) under the pre-surgical monitoring. Sequential interictal spikes in both genesis area and extended propagation pathway are collected, that, SSs genesis only in anterior hippocampus(aH)(possible propagation pathway in Entorhinal cortex(EC)), only in EC(possible propagation pathway in aH), and in both aH and EC synchronously. Instantaneous theta power was estimated by using Gabor wavelet transform, and theta power level was estimated by averaged over time and frequency before SSs(350ms pre-spike) and after SSs(350ms post-spike). The inhibitory effect around spikes was evaluated by the ratio of theta power level difference between pre-spike and post-spike to pre-spike theta power level. The findings were that theta power level was reduced across SSs, and the effects were more sever in the case of SSs in both aH and EC synchronously than either SSs only in EC or SSs only in aH. It is concluded that interictal spikes impair LFP theta rhythms transiently and directly. The work suggests that the reduction of theta power after the interictal spike might be an evaluation indicator of damage of epilepsy to human cognitive rhythms.

  16. Adaptive coupling optimized spiking coherence and synchronization in Newman-Watts neuronal networks.

    Science.gov (United States)

    Gong, Yubing; Xu, Bo; Wu, Ya'nan

    2013-09-01

    In this paper, we have numerically studied the effect of adaptive coupling on the temporal coherence and synchronization of spiking activity in Newman-Watts Hodgkin-Huxley neuronal networks. It is found that random shortcuts can enhance the spiking synchronization more rapidly when the increment speed of adaptive coupling is increased and can optimize the temporal coherence of spikes only when the increment speed of adaptive coupling is appropriate. It is also found that adaptive coupling strength can enhance the synchronization of spikes and can optimize the temporal coherence of spikes when random shortcuts are appropriate. These results show that adaptive coupling has a big influence on random shortcuts related spiking activity and can enhance and optimize the temporal coherence and synchronization of spiking activity of the network. These findings can help better understand the roles of adaptive coupling for improving the information processing and transmission in neural systems.

  17. Contamination spike simulation and measurement in a clean metal vapor laser

    International Nuclear Information System (INIS)

    Lin, C.E.; Yang, C.Y.

    1990-01-01

    This paper describes a new method for the generation of contamination-induced voltage spikes in a clean metal vapor laser. The method facilitates the study of the characteristics of this troublesome phenomenon in laser systems. Analysis of these artificially generated dirt spikes shows that the breakdown time of the laser tube is increased when these spike appear. The concept of a Townsend discharge is used to identify the parameter which changes the breakdown time of the discharges. The residual ionization control method is proposed to generate dirt spikes in a clean laser. Experimental results show that a wide range of dirt spike magnitudes can be obtained by using the proposed method. The method provides easy and accurate control of the magnitude of the dirt spike, and the laser tube does not become polluted. Results based on the measurements can be used in actual laser systems to monitor the appearance of dirt spikes and thus avoid the danger of thyratron failure

  18. Federal Support for Preserve America

    Science.gov (United States)

    Mississippi's Heritage Tourism Industry Post Hurricane Katrina also received a $150,000 Preserve America Grant Arkansas Delta, one for music, one for African-American history, and one for agriculture. The project will

  19. Cultural Preservation Program for Alaska

    Science.gov (United States)

    Barbaran, Francisco Ramon

    2011-01-01

    In this technical report, an innovative cultural preservation program for implementation in Athabascan villages is presented. The parameters for success in implementing such a project is discussed based on a workshop with Athabascan elders.

  20. Spiking cortical model based non-local means method for despeckling multiframe optical coherence tomography data

    Science.gov (United States)

    Gu, Yameng; Zhang, Xuming

    2017-05-01

    Optical coherence tomography (OCT) images are severely degraded by speckle noise. Existing methods for despeckling multiframe OCT data cannot deliver sufficient speckle suppression while preserving image details well. To address this problem, the spiking cortical model (SCM) based non-local means (NLM) method has been proposed in this letter. In the proposed method, the considered frame and two neighboring frames are input into three SCMs to generate the temporal series of pulse outputs. The normalized moment of inertia (NMI) of the considered patches in the pulse outputs is extracted to represent the rotational and scaling invariant features of the corresponding patches in each frame. The pixel similarity is computed based on the Euclidean distance between the NMI features and used as the weight. Each pixel in the considered frame is restored by the weighted averaging of all pixels in the pre-defined search window in the three frames. Experiments on the real multiframe OCT data of the pig eye demonstrate the advantage of the proposed method over the frame averaging method, the multiscale sparsity based tomographic denoising method, the wavelet-based method and the traditional NLM method in terms of visual inspection and objective metrics such as signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), equivalent number of looks (ENL) and cross-correlation (XCOR).

  1. Dopamine-signalled reward predictions generated by competitive excitation and inhibition in a spiking neural network model

    Directory of Open Access Journals (Sweden)

    Paul eChorley

    2011-05-01

    Full Text Available Dopaminergic neurons in the mammalian substantia nigra displaycharacteristic phasic responses to stimuli which reliably predict thereceipt of primary rewards. These responses have been suggested toencode reward prediction-errors similar to those used in reinforcementlearning. Here, we propose a model of dopaminergic activity in whichprediction error signals are generated by the joint action ofshort-latency excitation and long-latency inhibition, in a networkundergoing dopaminergic neuromodulation of both spike-timing dependentsynaptic plasticity and neuronal excitability. In contrast toprevious models, sensitivity to recent events is maintained by theselective modification of specific striatal synapses, efferent tocortical neurons exhibiting stimulus-specific, temporally extendedactivity patterns. Our model shows, in the presence of significantbackground activity, (i a shift in dopaminergic response from rewardto reward predicting stimuli, (ii preservation of a response tounexpected rewards, and (iii a precisely-timed below-baseline dip inactivity observed when expected rewards are omitted.

  2. Pasteurization: A reliable method for preservation of nutrient in seawater samples for inter-laboratory and field applications

    OpenAIRE

    Daniel, Anne; Kerouel, Roger; Aminot, Alain

    2012-01-01

    Following previous work, the production of reference material for nutrients in seawater, using pasteurization as a preservation method, was carried out seven times between 2006 and 2010 in the framework of inter-laboratory exercises. The preparation of samples from natural seawater allowed to become depleted in nutrients then spiked, bottled and pasteurized, is described. Five main nutrients are involved in this study: ammonium, nitrite, nitrate, phosphate and silicate. Bottles are in glass f...

  3. Fertility Preservation for Children Diagnosed with Cancer

    Medline Plus

    Full Text Available ... options further? Fertility Preservation - Where Does It Fit? Options for Fertility Preservation The following diagram gives a brief description of fertility preservation options available to children diagnosed with cancer before and ...

  4. Hereditary History Preserving Bisimilarity Is Undecidable

    DEFF Research Database (Denmark)

    Jurdzinski, Marcin; Nielsen, Mogens

    2000-01-01

    History preserving bisimilarity (hp-bisimilarity) and hereditary history preserving bisimilarity (hhp-bisimilarity) are behavioural equivalences taking into account causal relationships between events of concurrent systems. Their prominent feature is being preserved under action refinement...

  5. Fertility Preservation for Children Diagnosed with Cancer

    Science.gov (United States)

    ... Skip to main content SaveMyFertility An Online Fertility Preservation Toolkit for Patients and Their Providers Open menu ... with Cancer You are here Home » Patients Fertility Preservation for Children Diagnosed with Cancer Fertility Preservation for ...

  6. Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface.

    Science.gov (United States)

    Merolla, Paul A; Arthur, John V; Alvarez-Icaza, Rodrigo; Cassidy, Andrew S; Sawada, Jun; Akopyan, Filipp; Jackson, Bryan L; Imam, Nabil; Guo, Chen; Nakamura, Yutaka; Brezzo, Bernard; Vo, Ivan; Esser, Steven K; Appuswamy, Rathinakumar; Taba, Brian; Amir, Arnon; Flickner, Myron D; Risk, William P; Manohar, Rajit; Modha, Dharmendra S

    2014-08-08

    Inspired by the brain's structure, we have developed an efficient, scalable, and flexible non-von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts. Copyright © 2014, American Association for the Advancement of Science.

  7. Extraction of Plutonium From Spiked INEEL Soil Samples Using the Ligand-Assisted Supercritical Fluid Extraction (LA-SFE) Technique

    International Nuclear Information System (INIS)

    Fox, R.V.; Mincher, B.J.; Holmes, R.G.G.

    1999-01-01

    In order to investigate the effectiveness of ligand-assisted supercritical fluid extraction for the removal of transuranic contaminations from soils an Idaho National Engineering and Environmental Laboratory (INEEL) silty-clay soil sample was obtained from near the Radioactive Waste Management Complex area and subjected to three different chemical preparations before being spiked with plutonium. The spiked INEEL soil samples were subjected to a sequential aqueous extraction procedure to determine radionuclide portioning in each sample. Results from those extractions demonstrate that plutonium consistently partitioned into the residual fraction across all three INEEL soil preparations whereas americium partitioned 73% into the iron/manganese fraction for soil preparation A, with the balance partitioning into the residual fraction. Plutonium and americium were extracted from the INEEL soil samples using a ligand-assisted supercritical fluid extraction technique. Initial supercritical fluid extraction runs produced plutonium extraction technique. Initial supercritical fluid extraction runs produced plutonium extraction efficiencies ranging from 14% to 19%. After a second round wherein the initial extraction parameters were changed, the plutonium extraction efficiencies increased to 60% and as high as 80% with the americium level in the post-extracted soil samples dropping near to the detection limits. The third round of experiments are currently underway. These results demonstrate that the ligand-assisted supercritical fluid extraction technique can effectively extract plutonium from the spiked INEEL soil preparations

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

    Science.gov (United States)

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

    2013-07-01

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

  9. Fertility Preservation for Children Diagnosed with Cancer

    Medline Plus

    Full Text Available ... You are here Home » Patients Fertility Preservation for Children Diagnosed with Cancer Fertility Preservation for Children Diagnosed with Cancer Ask Your Doctor Information for ...

  10. Fertility Preservation for Children Diagnosed with Cancer

    Medline Plus

    Full Text Available ... Home » Patients Fertility Preservation for Children Diagnosed with Cancer Fertility Preservation for Children Diagnosed with Cancer Ask Your Doctor Information for Patients Many adult ...

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

    International Nuclear Information System (INIS)

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

    2004-01-01

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

  12. Systematic Regional Variations in Purkinje Cell Spiking Patterns

    Science.gov (United States)

    Xiao, Jianqiang; Cerminara, Nadia L.; Kotsurovskyy, Yuriy; Aoki, Hanako; Burroughs, Amelia; Wise, Andrew K.; Luo, Yuanjun; Marshall, Sarah P.; Sugihara, Izumi; Apps, Richard; Lang, Eric J.

    2014-01-01

    In contrast to the uniform anatomy of the cerebellar cortex, molecular and physiological studies indicate that significant differences exist between cortical regions, suggesting that the spiking activity of Purkinje cells (PCs) in different regions could also show distinct characteristics. To investigate this possibility we obtained extracellular recordings from PCs in different zebrin bands in crus IIa and vermis lobules VIII and IX in anesthetized rats in order to compare PC firing characteristics between zebrin positive (Z+) and negative (Z−) bands. In addition, we analyzed recordings from PCs in the A2 and C1 zones of several lobules in the posterior lobe, which largely contain Z+ and Z− PCs, respectively. In both datasets significant differences in simple spike (SS) activity were observed between cortical regions. Specifically, Z− and C1 PCs had higher SS firing rates than Z+ and A2 PCs, respectively. The irregularity of SS firing (as assessed by measures of interspike interval distribution) was greater in Z+ bands in both absolute and relative terms. The results regarding systematic variations in complex spike (CS) activity were less consistent, suggesting that while real differences can exist, they may be sensitive to other factors than the cortical location of the PC. However, differences in the interactions between SSs and CSs, including the post-CS pause in SSs and post-pause modulation of SSs, were also consistently observed between bands. Similar, though less strong trends were observed in the zonal recordings. These systematic variations in spontaneous firing characteristics of PCs between zebrin bands in vivo, raises the possibility that fundamental differences in information encoding exist between cerebellar cortical regions. PMID:25144311

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

    Science.gov (United States)

    Ostojic, Srdjan; Brunel, Nicolas

    2011-01-20

    Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

  14. Bio-inspired spiking neural network for nonlinear systems control.

    Science.gov (United States)

    Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

    2018-08-01

    Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. Systematic regional variations in Purkinje cell spiking patterns.

    Directory of Open Access Journals (Sweden)

    Jianqiang Xiao

    Full Text Available In contrast to the uniform anatomy of the cerebellar cortex, molecular and physiological studies indicate that significant differences exist between cortical regions, suggesting that the spiking activity of Purkinje cells (PCs in different regions could also show distinct characteristics. To investigate this possibility we obtained extracellular recordings from PCs in different zebrin bands in crus IIa and vermis lobules VIII and IX in anesthetized rats in order to compare PC firing characteristics between zebrin positive (Z+ and negative (Z- bands. In addition, we analyzed recordings from PCs in the A2 and C1 zones of several lobules in the posterior lobe, which largely contain Z+ and Z- PCs, respectively. In both datasets significant differences in simple spike (SS activity were observed between cortical regions. Specifically, Z- and C1 PCs had higher SS firing rates than Z+ and A2 PCs, respectively. The irregularity of SS firing (as assessed by measures of interspike interval distribution was greater in Z+ bands in both absolute and relative terms. The results regarding systematic variations in complex spike (CS activity were less consistent, suggesting that while real differences can exist, they may be sensitive to other factors than the cortical location of the PC. However, differences in the interactions between SSs and CSs, including the post-CS pause in SSs and post-pause modulation of SSs, were also consistently observed between bands. Similar, though less strong trends were observed in the zonal recordings. These systematic variations in spontaneous firing characteristics of PCs between zebrin bands in vivo, raises the possibility that fundamental differences in information encoding exist between cerebellar cortical regions.

  16. 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.

  17. Routes to Chaos Induced by a Discontinuous Resetting Process in a Hybrid Spiking Neuron Model.

    Science.gov (United States)

    Nobukawa, Sou; Nishimura, Haruhiko; Yamanishi, Teruya

    2018-01-10

    Several hybrid spiking neuron models combining continuous spike generation mechanisms and discontinuous resetting processes following spiking have been proposed. The Izhikevich neuron model, for example, can reproduce many spiking patterns. This model clearly possesses various types of bifurcations and routes to chaos under the effect of a state-dependent jump in the resetting process. In this study, we focus further on the relation between chaotic behaviour and the state-dependent jump, approaching the subject by comparing spiking neuron model versions with and without the resetting process. We first adopt a continuous two-dimensional spiking neuron model in which the orbit in the spiking state does not exhibit divergent behaviour. We then insert the resetting process into the model. An evaluation using the Lyapunov exponent with a saltation matrix and a characteristic multiplier of the Poincar'e map reveals that two types of chaotic behaviour (i.e. bursting chaotic spikes and near-period-two chaotic spikes) are induced by the resetting process. In addition, we confirm that this chaotic bursting state is generated from the periodic spiking state because of the slow- and fast-scale dynamics that arise when jumping to the hyperpolarization and depolarization regions, respectively.

  18. Stimulus Sensitivity of a Spiking Neural Network Model

    Science.gov (United States)

    Chevallier, Julien

    2018-02-01

    Some recent papers relate the criticality of complex systems to their maximal capacity of information processing. In the present paper, we consider high dimensional point processes, known as age-dependent Hawkes processes, which have been used to model spiking neural networks. Using mean-field approximation, the response of the network to a stimulus is computed and we provide a notion of stimulus sensitivity. It appears that the maximal sensitivity is achieved in the sub-critical regime, yet almost critical for a range of biologically relevant parameters.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

    Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed...... in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods...

  20. 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.