scholarly journals Generating surrogates for significance estimation of spatio-temporal spike patterns

2021 ◽  
Author(s):  
Alessandra Stella ◽  
Peter Bouss ◽  
Günther Palm ◽  
Sonja Grün
Keyword(s):  
Experimental Data ◽  
Stationary Point ◽  
Original Data ◽  
Surrogate Data ◽  
False Positives ◽  
Statistical Properties ◽  
Frequent Itemset ◽  
Ground Truth Data ◽  
Spatio Temporal ◽  
Spike Patterns

Spatio-temporal spike patterns were suggested as indications of active cell assemblies. We developed the SPADE method to detect significant spatio-temporal patterns (STPs) with ms accuracy. STPs are defined as identically repeating spike patterns across neurons with temporal delays between the spikes. The significance of STPs is derived by comparison to the null-hypothesis of independence implemented by surrogate data. SPADE binarizes the spike trains and examines the data for STPs by counting repeated patterns using frequent itemset mining. The significance of STPs is evaluated by comparison to pattern counts derived from surrogate data, i.e., modifications of the original data with destroyed potential spike correlations but under conservation of the firing rate profiles. To avoid erroneous results, surrogate data are required to retain the statistical properties of the original data as much as possible. A classically chosen surrogate technique is Uniform Dithering (UD), which displaces each spike independently according to a uniform distribution. We find that binarized UD surrogates of our experimental data (motor cortex) contain fewer spikes than the binarized original data. As a consequence, false positives occur. Here, we identify the reason for the spike reduction, which is the lack of conservation of short ISIs. To overcome this problem, we study five alternative surrogate techniques and examine their statistical properties such as spike loss, ISI characteristics, effective movement of spikes, and arising false positives when applied to different ground truth data sets: first, on stationary point process models, and then on non-stationary point processes mimicking statistical properties of experimental data. We conclude that trial-shifting best preserves the features of the original data and has a low expected false-positive rate. Finally, the analysis of the experimental data provides consistent STPs across the alternative surrogates.

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

scholarly journals Monitoring of rhythms in laser speckle data

2014 ◽  
Vol 07 (03) ◽  
pp. 1450015 ◽  
Author(s):  
D. E. Postnov ◽  
A. Y. Neganova ◽  
D. D. Postnov ◽  
A. R. Brazhe
Keyword(s):  
Blood Flow ◽  
Graphics Processing Units ◽  
Surrogate Data ◽  
Laser Speckle ◽  
Tubuloglomerular Feedback ◽  
Additional Information ◽  
3D Data ◽  
Kidney Blood Flow ◽  
Rhythm Analysis ◽  
Spatio Temporal

While the laser speckle imaging (LSI) is a powerful tool for multiple biomedical applications, such as monitoring of the blood flow, in many cases it can provide additional information when combined with spatio-temporal rhythm analysis. We demonstrate the application of Graphics Processing Units (GPU)-based rhythm analysis for the post processing of LSI data, discuss the relevant structure of GPU-based computations, test the proposed technique on surrogate 3D data, and apply this approach to kidney blood flow autoregulation. Experiments with surrogate data demonstrate the ability of the method to extract information about oscillation patterns from noisy data, as well as to detect the moving source of the rhythm. The analysis of kidney data allow us to detect and to localize the dynamics arising from autoregulation processes at the level of individual nephrons (tubuloglomerular feedback (TGF) rhythm), as well as to distinguish between the TGF-active and the TGF-silent zones.

A Single Technically Consistent Design Formula for the Thickness of Cylindrical Sections Under Internal Pressure

2006 ◽  
Vol 129 (1) ◽  
pp. 211-215 ◽  
Author(s):  
John D. Fishburn
Keyword(s):  
Experimental Data ◽  
Internal Pressure ◽  
State Of The Art ◽  
Original Data ◽  
Pressure Vessels ◽  
Time Dependent ◽  
Design Codes ◽  
Recent Developments ◽  
Current Design ◽  
Single Formula

Within the current design codes for boilers, piping, and pressure vessels, there are many different equations for the thickness of a cylindrical section under internal pressure. A reassessment of these various formulations, using the original data, is described together with more recent developments in the state of the art. A single formula, which can be demonstrated to retain the same design margin in both the time-dependent and time-independent regimes, is shown to give the best correlation with the experimental data and is proposed for consideration for inclusion in the design codes.

scholarly journals Nonlinear Dynamic Identification of Beams Resting on Nonlinear Viscoelastic Foundations Based on the Time-Delayed Transfer Entropy and Improved Surrogate Data Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Guohua Liu ◽  
Changpeng Ye ◽  
Xu Liang ◽  
Zhongkai Xie ◽  
Ziyuan Yu
Keyword(s):  
Lateral Displacement ◽  
Time History ◽  
Original Data ◽  
Surrogate Data ◽  
Transfer Entropy ◽  
Dynamic Identification ◽  
Concentrated Load ◽  
Nonlinear Foundation ◽  
Young’S Moduli ◽  
The Galerkin Method

In this work, the transfer entropy and surrogate data algorithm were introduced to identify the nonlinearity level of the system by using a numerical solution of nonlinear response of beams. A homogeneous Euler-Bernoulli beam was subjected to a time-varying concentrated load and resting on a nonlinear foundation. The Galerkin method was applied to discretize the dimensionless differential governing equation of the forced vibration, and then the fourth-order Runge-Kutta method was used to obtain the time-history response of the lateral displacement. In order to simulate different nonlinearity levels, different ratios between nonlinear parameters and linear parameters of foundation, as well as different Young’s moduli, were used. A nonlinearity index was proposed. In the case of different nonlinearity levels, the nonlinearity index was used to analyze the difference between the transfer entropy calculated from the original data and the transfer entropy calculated from the surrogate data. By comparing and analyzing the nonlinearity index values under different ratios, it was found that the nonlinearity index values generally increased with the increase of the ratio and the sum of nonlinearity index values had a positive correlation with the ratio. By comparing the nonlinearity index values of the transfer entropy results of beams with different Young's moduli, it was found that the sum of the nonlinearity index values generally decreased with the increase of Young's modulus. The numerical results demonstrate that the present approach could effectively quantify the nonlinearity in the response of a beam resting on a nonlinear foundation.

A Survey of P-V-T Data for Water in the Critical Region

1961 ◽  
Vol 83 (1) ◽  
pp. 1-13 ◽  
Author(s):  
E. S. Nowak ◽  
R. J. Grosh ◽  
P. E. Liley
Keyword(s):  
Experimental Data ◽  
Critical Region ◽  
Volume Expansion ◽  
Superheated Steam ◽  
Original Data ◽  
Comprehensive Review ◽  
Superheated Vapor ◽  
Specific Volumes ◽  
Original Experimental Data

A comprehensive review is given of P-V-T data available for water at pressures from 2700 to 4000 psia, specific volumes from 0.025 to 0.125 cu ft per lb, and temperatures from about 680 to 800 deg F. Experimental and computed values of various investigations are given. A description of the various methods and formulas used to interpolate the original data is also given. The results indicate there is a lack of smoothed or computed values for the specific volumes of the superheated vapor at sufficiently close intervals in the critical region. This makes it difficult to interpolate accurately and derive such properties as density and coefficients of volume expansion and compressibility. Furthermore, there are significant differences between the data for superheated steam given in the various tables which can be attributed to erroneous methods of interpolating the original experimental data.

scholarly journals Dynamic spatio-temporal generation of large-scale synthetic gridded precipitation: with improved spatial coherence of extremes

2019 ◽  
Vol 34 (9) ◽  
pp. 1369-1383 ◽  
Author(s):  
Dirk Diederen ◽  
Ye Liu
Keyword(s):  
Large Scale ◽  
Spatial Coherence ◽  
Original Data ◽  
Return Level ◽  
Data Sets ◽  
Large Set ◽  
Precipitation Data ◽  
Data Set ◽  
Spatio Temporal ◽  
Synthetic Precipitation

Abstract With the ongoing development of distributed hydrological models, flood risk analysis calls for synthetic, gridded precipitation data sets. The availability of large, coherent, gridded re-analysis data sets in combination with the increase in computational power, accommodates the development of new methodology to generate such synthetic data. We tracked moving precipitation fields and classified them using self-organising maps. For each class, we fitted a multivariate mixture model and generated a large set of synthetic, coherent descriptors, which we used to reconstruct moving synthetic precipitation fields. We introduced randomness in the original data set by replacing the observed precipitation fields in the original data set with the synthetic precipitation fields. The output is a continuous, gridded, hourly precipitation data set of a much longer duration, containing physically plausible and spatio-temporally coherent precipitation events. The proposed methodology implicitly provides an important improvement in the spatial coherence of precipitation extremes. We investigate the issue of unrealistic, sudden changes on the grid and demonstrate how a dynamic spatio-temporal generator can provide spatial smoothness in the probability distribution parameters and hence in the return level estimates.

scholarly journals Simulating tubulin-associated unit transport in an axon: using bootstrapping for estimating confidence intervals of best-fit parameter values obtained from indirect experimental data

2017 ◽  
Vol 473 (2201) ◽  
pp. 20170045 ◽  
Author(s):  
I. A. Kuznetsov ◽  
A. V. Kuznetsov
Keyword(s):  
Experimental Data ◽  
Confidence Intervals ◽  
Surrogate Data ◽  
Model Parameters ◽  
Model Predictions ◽  
Estimate Parameter ◽  
Minimum Number ◽  
Percentile Bootstrap ◽  
Parameter Values ◽  
Best Fit

In this paper, we first develop a model of axonal transport of tubulin-associated unit (tau) protein. We determine the minimum number of parameters necessary to reproduce published experimental results, reducing the number of parameters from 18 in the full model to eight in the simplified model. We then address the following questions: Is it possible to estimate parameter values for this model using the very limited amount of published experimental data? Furthermore, is it possible to estimate confidence intervals for the determined parameters? The idea that is explored in this paper is based on using bootstrapping. Model parameters were estimated by minimizing the objective function that simulates the discrepancy between the model predictions and experimental data. Residuals were then identified by calculating the differences between the experimental data and model predictions. New, surrogate ‘experimental’ data were generated by randomly resampling residuals. By finding sets of best-fit parameters for a large number of surrogate data the histograms for the model parameters were produced. These histograms were then used to estimate confidence intervals for the model parameters, by using the percentile bootstrap. Once the model was calibrated, we applied it to analysing some features of tau transport that are not accessible to current experimental techniques.

scholarly journals Spatio-temporal dependencies in functional connectivity in rodent cortical cultures

2011 ◽  
Vol 2 (3) ◽  
Author(s):  
Matthew C. Spencer ◽  
Julia H. Downes ◽  
Dimitris Xydas ◽  
Mark W. Hammond ◽  
Victor M. Becerra ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Structures ◽  
Surrogate Data ◽  
Model Driven ◽  
Functional Connections ◽  
Co Activation ◽  
Cortical Cultures ◽  
Recorded Activity ◽  
Spatio Temporal ◽  
Neuronal Systems

AbstractModels of functional connectivity in cortical cultures on multi-electrodes arrays may aid in understanding how cognitive pathways form and improve techniques that aim to interface with neuronal systems. To enable research on such models, this study uses both data- and model-driven approaches to determine what dependencies are present in and between functional connectivity networks derived from bursts of extracellularly recorded activity. Properties of excitation in bursts were analysed using correlative techniques to assess the degree of linear dependence and then two parallel techniques were used to assess functional connectivity. Three models presenting increasing levels of spatio-temporal dependency were used to capture the dynamics of individual functional connections and their consistencies were verified using surrogate data. By comparing network-wide properties between model generated networks and functional networks from data, complex interdependencies were revealed. This indicates the persistent co-activation of neuronal pathways in spontaneous bursts, as can be found in whole brain structures.

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