Auto- Covariance and -Correlation Function Estimation for Spike Train ISIs

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Description

The function acf.spikeTrain computes (and by default plots) estimates of the autocovariance or autocorrelation function of the inter-spike intervals of a spike train.

Usage

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acf.spikeTrain(spikeTrain, lag.max = NULL,
       type = c("correlation", "covariance", "partial"),
       plot = TRUE, na.action = na.fail,
       demean = TRUE, xlab = "Lag (in isi #)",
       ylab = "ISI acf",
       main, ...)

Arguments

spikeTrain

a spikeTrain object or a vector which can be coerced to such an object.

lag.max

maximum lag at which to calculate the acf. Default is 10*log10(N) where N is the number of ISIs. Will be automatically limited to one less than the number of ISIs in the spike train.

type

character string giving the type of acf to be computed. Allowed values are "correlation" (the default), "covariance" or "partial".

plot

logical. If TRUE (the default) the acf is plotted.

na.action

function to be called to handle missing values. na.pass can be used.

demean

logical. Should the covariances be about the sample means?

xlab

x axis label.

ylab

y axis label.

main

title for the plot.

...

further arguments to be passed to plot.acf.

Details

Just a wrapper for acf function. The first argument, spikeTrain, is processed first to extract the inter-spike intervals. acf.spikeTrain is mainly used to plot what Perkel et al (1967) call the serial correlation coefficient (Eq. 8) or serial covariance coefficient (Eq. 7), p 400.

Value

An object of class "acf", which is a list with the following elements:

lag

A three dimensional array containing the lags at which the acf is estimated.

acf

An array with the same dimensions as lag containing the estimated acf.

type

The type of correlation (same as the type argument).

n.used

The number of observations in the time series.

series

The name of the series x.

snames

The series names for a multivariate time series.

The lag k value returned by ccf(x,y) estimates the correlation between x[t+k] and y[t].

The result is returned invisibly if plot is TRUE.

Author(s)

Christophe Pouzat christophe.pouzat@gmail.com

References

Perkel D. H., Gerstein, G. L. and Moore G. P. (1967) Neural Spike Trains and Stochastic Point Processes. I. The Single Spike Train. Biophys. J., 7: 391-418. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=4292791

See Also

acf, varianceTime, renewalTestPlot

Examples

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## Simulate a log normal train
train1 <- c(cumsum(rlnorm(301,log(0.01),0.25)))
train1 <- as.spikeTrain(train1)
## Get its isi acf
acf.spikeTrain(train1,lag.max=100)

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