psth: Compute and Plot Peri-Stimulus Time Histogram

In STAR: Spike Train Analysis with R

Description

psth computes and plot.psth plots a peri-stimulus time histogram (called PST, post-stimulus time histogram by Gerstein and Kiang (1960)) from repeated presentations of a stimulation. Confidence bands can be obtained using the Poisson approximation.

Usage

psth(repeatedTrain, breaks = 20, include.lowest = TRUE,
     right = TRUE, plot = TRUE, CI = 0.95, ...)
## S3 method for class 'psth'
plot(x, stimTimeCourse = NULL, colStim = "grey80",
          colCI = NULL, xlab, ylab, main, xlim, ylim, lwd = 2,
          col = 1, ...)

Arguments

repeatedTrain	a repeatedTrain object or a list which can be coerced to such an object.
x	a psth object.
stimTimeCourse	NULL (default) or a two elements vector specifying the time boundaries (in s) of a stimulus presentation.
colStim	the background color used for the stimulus.
breaks	a numeric. A single number is interpreted has the number of bins; a vector of length 2 is interpreted as the bin width and the step to use (see details); otherwise interpreted as the position of the "breaks" between bins.
include.lowest	corresponding argument of hist.
right	corresponding argument of hist.
plot	corresponding argument of hist.
CI	The coverage probability of the confidence intervals.
colCI	if not NULL (default) a confidence band is plotted with the specified color; two dashed lines are plotted otherwise.
xlim	a numeric (default value supplied). See plot.
ylim	a numeric (default value supplied). See plot.
xlab	a character (default value supplied). See plot.
ylab	a character (default value supplied). See plot.
main	a character (default value supplied). See plot.
lwd	line width used to plot the estimated density. See plot.
col	color used to plot the estimated density. See plot.
...	see plot.

Details

When confidence bands are requested they are obtained from the qunatiles of the Poisson distribution.

When a 2 elements vector is used as breaks argument it is interpreted as specifying a bin width (first element if elements are unnamed, "bw" element otherwise) and a step (second element if elements are unnamed, "step" element otherwise). The idea is then to obtain a smoother looking PSTH by counting spikes within overlapping bins. That is if the center of the ith bin is xi the one of the (i+1)th bin will be xi + step.

Value

When plot is set to FALSE in psth, a list of class psth is returned and no plot is generated. This list has the following components:

freq	a vector containing the instantaneous firing rate.
ciUp	a vector with the upper limit of the confidence band.
ciLow	a vector with the lower limit of the confidence band.
breaks	a numeric vector with the breaks in between which spikes were counted. Similar to the component of the same name returned by hist.
mids	a numeric vector with the mid points of breaks. Similar to the component of the same name returned by hist.
counts	a matrix with as many rows as components in repeatedTrain and as many columns as bins. Each element of the matrix contains the number of spikes falling in a given trial in a given bin.
nbTrials	the number of stimulations.
call	the matched call.

When plot is set to TRUE nothing is returned and a plot is generated as a side effect. Of course the same occurs upon calling plot.psth with a psth object argument.

Author(s)

Christophe Pouzat [e-mail]

References

Gerstein, George L. and Kiang, Nelson Y.-S. (1960) An Approach to the Quantitative Analysis of Electrophysiological Data from Single Neurons. Biophysical Journal 1: 15–28. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=13704760

Kalbfleisch, J. G. (1985) Probability and Statistical Inference. Volume 2: Statistical Inference. Springer-Verlag.

See Also

as.repeatedTrain, is.repeatedTrain, print.repeatedTrain, summary.repeatedTrain, raster

Examples

## Load Vanillin responses data (first cockroach data set)
data(CAL1V)
## convert them into repeatedTrain objects
## The stimulus command is on between 4.49 s and 4.99s
CAL1V <- lapply(CAL1V,as.repeatedTrain)
## look at the individual raster plots
plot(CAL1V[["neuron 1"]],stimTimeCourse=c(4.49,4.99),main="N1")
## Create a simple black and white PSTH for neuron 1
psth(CAL1V[["neuron 1"]],stimTimeCourse=c(4.49,4.99),breaks=20)
## Rebuilt the same PSTH but with red confidence bands
psth(CAL1V[["neuron 1"]],stimTimeCourse=c(4.49,4.99),breaks=20,colCI=2)
## Make the PSTH smoother
psth(CAL1V[["neuron 1"]],stimTimeCourse=c(4.49,4.99),breaks=c(bw=0.5,step=0.05),colCI=2)
## Make a plot with PSTHs from 4 neurons superposed
## First get lists containing PSTHs from each neuron
psth1 <- psth(CAL1V[["neuron 1"]],breaks=c(bw=0.5,step=0.05),plot=FALSE)
psth2 <- psth(CAL1V[["neuron 2"]],breaks=c(bw=1,step=0.1),plot=FALSE)
psth3 <- psth(CAL1V[["neuron 3"]],breaks=c(bw=0.5,step=0.05),plot=FALSE)
psth4 <- psth(CAL1V[["neuron 4"]],breaks=c(bw=2,step=0.2),plot=FALSE)
## Get the maximal frequency to display
maxFreq <- max(max(psth1$ciUp),max(psth2$ciUp),max(psth3$ciUp),max(psth4$ciUp))
## Build plot
plot(c(0,10),c(0,75),type="n",
     xaxs="i",yaxs="i",xlab="Time (s)",
     ylab="Freq. (Hz)",
     main="PSTHs from 4 simultaneously recorded neurons",
     sub="20 stimulations with vanillin were used.")
## Add rectangle corresponding to stimulation command
rect(4.49,0,4.99,75,col="grey80",lty=0)
## Add the neurons PSTHs as confidence bands
polygon(c(psth1$mids,rev(psth1$mids)),c(psth1$ciLow,rev(psth1$ciUp)),col=1,border=NA)
polygon(c(psth2$mids,rev(psth2$mids)),c(psth2$ciLow,rev(psth2$ciUp)),col=2,border=NA)
polygon(c(psth3$mids,rev(psth3$mids)),c(psth3$ciLow,rev(psth3$ciUp)),col=3,border=NA)
polygon(c(psth4$mids,rev(psth4$mids)),c(psth4$ciLow,rev(psth4$ciUp)),col=4,border=NA)
legend(0.1,maxFreq,legend=paste("neuron",1:4),lty=1,col=1:4,bty="n")

Example output

Loading required package: survival
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-20. For overview type 'help("mgcv-package")'.
Loading required package: R2HTML
Loading required package: gss
Loading required package: codetools

STAR documentation built on May 2, 2019, 11:44 a.m.