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

2013 ◽  
Vol 110 (2) ◽  
pp. 562-572 ◽  
Author(s):  
Claudio S. Quiroga-Lombard ◽  
Joachim Hass ◽  
Daniel Durstewitz
Keyword(s):  
Correlation Functions ◽  
Cross Correlation ◽  
Simulated Data ◽  
Weak Sense ◽  
Spike Trains ◽  
Data Sets ◽  
Stimulus Movement ◽  
Spike Train Data ◽  
Definition Of

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.

scholarly journals Kinetic Analysis of Drug–Target Interactions with PET for Characterization of Pharmacological Hysteresis

2013 ◽  
Vol 33 (5) ◽  
pp. 700-707 ◽  
Author(s):  
Cristian Salinas ◽  
David Weinzimmer ◽  
Graham Searle ◽  
David Labaree ◽  
Jim Ropchan ◽  
...  
Keyword(s):  
Simulated Data ◽  
Receptor Occupancy ◽  
Volume Of Distribution ◽  
Data Sets ◽  
Suitable Model ◽  
Dose Selection ◽  
Care Needs ◽  
Positron Emission

In vivo characterization of the brain pharmacokinetics of novel compounds provides important information for drug development decisions involving dose selection and the determination of administration regimes. In this context, the compound-target affinity is the key parameter to be estimated. However, if compounds exhibit a dynamic lag between plasma and target bound concentrations leading to pharmacological hysteresis, care needs to be taken to ensure the appropriate modeling approach is used so that the system is characterized correctly and that the resultant estimates of affinity are correct. This work focuses on characterizing different pharmacokinetic models that relate the plasma concentration to positron emission tomography outcomes measurements (e.g., volume of distribution and target occupancy) and their performance in estimating the true in vivo affinity. Measured (histamine H3 receptor antagonist—GSK189254) and simulated data sets enabled the investigation of different modeling approaches. An indirect pharmacokinetic-receptor occupancy model was identified as a suitable model for the calculation of affinity when a compound exhibits pharmacological hysteresis.

Decoding Poisson Spike Trains by Gaussian Filtering

2010 ◽  
Vol 22 (5) ◽  
pp. 1245-1271 ◽  
Author(s):  
Sidney R. Lehky
Keyword(s):  
Amplitude Spectrum ◽  
Spike Trains ◽  
Optimal Recovery ◽  
Rate Function ◽  
Gaussian Kernel ◽  
Gaussian Filtering ◽  
Low Pass ◽  
Spike Train Data ◽  
Fourier Amplitude Spectrum

The temporal waveform of neural activity is commonly estimated by low-pass filtering spike train data through convolution with a gaussian kernel. However, the criteria for selecting the gaussian width σ are not well understood. Given an ensemble of Poisson spike trains generated by an instantaneous firing rate function λ(t), the problem was to recover an optimal estimate of λ(t) by gaussian filtering. We provide equations describing the optimal value of σ using an error minimization criterion and examine how the optimal σ varies within a parameter space defining the statistics of inhomogeneous Poisson spike trains. The process was studied both analytically and through simulations. The rate functions λ(t) were randomly generated, with the three parameters defining spike statistics being the mean of λ(t), the variance of λ(t), and the exponent α of the Fourier amplitude spectrum 1/fα of λ(t). The value of σopt followed a power law as a function of the pooled mean interspike interval I, σopt = aIb, where a was inversely related to the coefficient of variation CV of λ(t), and b was inversely related to the Fourier spectrum exponent α. Besides applications for data analysis, optimal recovery of an analog signal waveform λ(t) from spike trains may also be useful in understanding neural signal processing in vivo.

Framework for Multivariate Selectivity Analysis, Part I: Theoretical and Practical Merits

2005 ◽  
Vol 59 (6) ◽  
pp. 787-803 ◽  
Author(s):  
Christopher D. Brown ◽  
Trent D. Ridder
Keyword(s):  
Near Infrared ◽  
Clinical Laboratory ◽  
Simulated Data ◽  
Calibration Method ◽  
National Committee ◽  
Data Sets ◽  
Calibration Methods ◽  
Inverse Calibration ◽  
The One ◽  
Definition Of

A number of definitions of multivariate selectivity have been proposed in the literature. Arguably, the one that enjoys the greatest chemometric attention has been the net analyte signal (NAS) based definitions of Lorber and Zinn. Recent works have suggested that similar inference can be made for inverse least-squares calibration methods (e.g., principal components regression). However, the properties of inverse calibration methods are markedly different than classical methods, so in many practical cases involving inverse models classically derived figures of merit cannot be transparently interpreted. In Part I of this work, we discuss a selectivity framework that is theoretically consistent regardless of the calibration method. Importantly, it is also experimentally measurable, either through controlled selectivity experiments, or through analysis on opportunistically acquired sample measurements. It is statistically advantageous to use the former if such control is achievable. Selectivity is defined to be a function of the change in predicted analyte concentration that will result from a change in the concentration of an interferant, an approach consistent with traditional definitions of analytical selectivity and National Committee for Clinical Laboratory Standards recommendations for interference testing. Unlike the NAS-based definition of selectivity, the definition discussed herein is relevant to only a particular analyte–interferant pair. The theoretical and experimental aspects of this approach are illustrated with simulated data herein and in Part II of this paper, which investigates several experimental near-infrared data sets.

scholarly journals Extending balance assessment for the generalized propensity score under multiple imputation

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Anna-Simone J. Frank ◽  
David S. Matteson ◽  
Hiroko K. Solvang ◽  
Angela Lupattelli ◽  
Hedvig Nordeng
Keyword(s):  
Propensity Score ◽  
Multiple Imputation ◽  
Simulated Data ◽  
Missing At Random ◽  
Estimation Bias ◽  
Data Sets ◽  
Balance Assessment ◽  
Generalized Propensity Score ◽  
Standardized Mean Difference ◽  
Definition Of

AbstractThis manuscript extends the definition of the Absolute Standardized Mean Difference (ASMD) for binary exposure (M = 2) to cases for M > 2 on multiple imputed data sets. The Maximal Maximized Standardized Difference (MMSD) and the Maximal Averaged Standardized Difference (MASD) were proposed. For different percentages, missing data were introduced in covariates in the simulated data based on the missing at random (MAR) assumption. We then investigate the performance of these two metric definitions using simulated data of full and imputed data sets. The performance of the MASD and the MMSD were validated by relating the balance metrics to estimation bias. The results show that there is an association between the balance metrics and bias. The proposed balance diagnostics seem therefore appropriate to assess balance for the generalized propensity score (GPS) under multiple imputation.

Generation of Spike Trains with Controlled Auto- and Cross-Correlation Functions

2009 ◽  
Vol 21 (6) ◽  
pp. 1642-1664 ◽  
Author(s):  
Michael Krumin ◽  
Shy Shoham
Keyword(s):  
Correlation Functions ◽  
Point Processes ◽  
Cross Correlation ◽  
Spike Trains ◽  
Correlation Structure ◽  
Distributed Processes ◽  
New Strategy ◽  
Neuronal Stimulation ◽  
Analytical Formulas ◽  
Rate Processes

Emerging evidence indicates that information processing, as well as learning and memory processes, in both the network and single-neuron levels are highly dependent on the correlation structure of multiple spike trains. Contemporary experimental as well as theoretical studies that involve quasi-realistic neuronal stimulation thus require a method for controlling spike train correlations. This letter introduces a general new strategy for generating multiple spike trains with exactly controlled mean firing rates and correlation structure (defined in terms of auto- and cross-correlation functions). Our approach nonlinearly transforms random gaussian-distributed processes with a predistorted correlation structure into nonnegative rate processes, which are then used to generate doubly stochastic Poisson point processes with the required correlation structure. We show how this approach can be used to generate stationary or nonstationary spike trains from small or large groups of neurons with diverse auto- and cross-correlation structures. We analyze and derive analytical formulas for the high-order correlation structure of generated spike trains and discuss the limitations of this approach.

scholarly journals netprioR: a probabilistic model for integrative hit prioritisation of genetic screens

2019 ◽  
Vol 18 (3) ◽  
Author(s):  
Fabian Schmich ◽  
Jack Kuipers ◽  
Gunter Merdes ◽  
Niko Beerenwinkel
Keyword(s):  
Rna Interference ◽  
Prior Knowledge ◽  
Simulated Data ◽  
R Package ◽  
Heterogeneous Data ◽  
Biological Data ◽  
Network Data ◽  
Data Sets ◽  
Screening Experiments

Abstract In the post-genomic era of big data in biology, computational approaches to integrate multiple heterogeneous data sets become increasingly important. Despite the availability of large amounts of omics data, the prioritisation of genes relevant for a specific functional pathway based on genetic screening experiments, remains a challenging task. Here, we introduce netprioR, a probabilistic generative model for semi-supervised integrative prioritisation of hit genes. The model integrates multiple network data sets representing gene–gene similarities and prior knowledge about gene functions from the literature with gene-based covariates, such as phenotypes measured in genetic perturbation screens, for example, by RNA interference or CRISPR/Cas9. We evaluate netprioR on simulated data and show that the model outperforms current state-of-the-art methods in many scenarios and is on par otherwise. In an application to real biological data, we integrate 22 network data sets, 1784 prior knowledge class labels and 3840 RNA interference phenotypes in order to prioritise novel regulators of Notch signalling in Drosophila melanogaster. The biological relevance of our predictions is evaluated using in silico and in vivo experiments. An efficient implementation of netprioR is available as an R package at http://bioconductor.org/packages/netprioR.

scholarly journals Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering

2004 ◽  
Vol 16 (8) ◽  
pp. 1661-1687 ◽  
Author(s):  
R. Quian Quiroga ◽  
Z. Nadasdy ◽  
Y. Ben-Shaul
Keyword(s):  
Simulated Data ◽  
New Method ◽  
Data Sets ◽  
Improved Method ◽  
Spike Detection ◽  
Gaussian Distributions ◽  
Conventional Methods ◽  
Simulated Data Sets

This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wave let transform, which localizes distinctive spike features, with super paramagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.

scholarly journals Inferring the collective dynamics of neuronal populations from single-trial spike trains using mechanistic models

2019 ◽  
Author(s):  
Christian Donner ◽  
Manfred Opper ◽  
Josef Ladenbauer
Keyword(s):  
Population Dynamics ◽  
Spike Train ◽  
Spike Trains ◽  
Neuronal Membrane ◽  
Model Parameters ◽  
Mechanistic Models ◽  
Single Trial ◽  
Experimental Conditions ◽  
Spike Train Data

AbstractMulti-neuronal spike-train data recorded in vivo often exhibit rich dynamics as well as considerable variability across cells and repetitions of identical experimental conditions (trials). Efforts to characterize and predict the population dynamics and the contributions of individual neurons require model-based tools. Abstract statistical models allow for principled parameter estimation and model selection, but possess only limited interpretive power because they typically do not incorporate prior biophysical constraints. Here we present a statistically principled approach based on a population of doubly-stochastic integrate-and-fire neurons, taking into account basic biophysics. This model class comprises an idealized description for the dynamics of the neuronal membrane voltage in response to fast independent and slower shared input fluctuations. To efficiently estimate the model parameters and compare different model variants we compute the likelihood of observed single-trail spike trains by leveraging analytical methods for spiking neuron models combined with inference techniques for hidden Markov models. This allows us to reconstruct the shared input variations, classify their dynamics, obtain precise spike rate estimates, and quantify how individual neurons couple to the low-dimensional overall population dynamics, all from a single trial. Extensive evaluations based on simulated data show that our method correctly identifies the dynamics of the shared input process and accurately estimates the model parameters. Validations on ground truth recordings of neurons in vitro demonstrate that our approach successfully reconstructs the dynamics of hidden inputs and yields improved fits compared to a typical phenomenological model. Finally, we apply the method to a neuronal population recorded in vivo, for which we assess the contributions of individual neurons to the overall spiking dynamics. Altogether, our work provides statistical inference tools for a class of reasonably constrained, mechanistic models and demonstrates the benefits of this approach to analyze measured spike train data.

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