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Last data update: 2014.03.03

R: Related Functions and Methods for Joint-PSTHs and Joint...

jpsth

R Documentation

Related Functions and Methods for Joint-PSTHs and Joint Scatter Diagrams

Description

Some mainly graphical tools to probe interactions between 2 neurons recorded in the presence of a repeated stimulation.

Usage

jsd(xRT, yRT, acquisitionWindow, xlab, ylab,
    main, pch = ".", ...)
jpsth(xRT, yRT, xBreaks, yBreaks,
      acquisitionWindow, nbEvtPerBin = 50)
## S3 method for class 'jpsth'
contour(x, xlab, ylab, main, ...)
## S3 method for class 'jpsth'
image(x, xlab, ylab, main, ...)
## S3 method for class 'jpsth'
persp(x, xlab, ylab, main, ...)
jpsth2df(object)

Arguments

`xRT`	a `repeatedTrain` object whose spike times will appear on the abscissa of the plots.
`yRT`	a `repeatedTrain` object whose spike times will appear on the ordinate of the plots. It must have the same length as `xRT`.
`x, object`	`jpsth` objects.
`xBreaks, yBreaks`	A single number (the bin width) or a vector defining bins boundaries on the X and Y axis. If missing a default is provided.
`acquisitionWindow`	a 2 elements vector specifying the begining and the end of the acquisition. If missing values are obtained using the `floor` of the smallest spike time and the `ceiling` of the largest one.
`nbEvtPerBin`	If both `xBreaks` and `xBreaks` are missing a bin width, `bw`, is computed such that the expected value of the count per cell (2 dimensional bin) would be `nbEvtPerBin` assuming a stationary Poisson discharge for both neurons.
`xlab`	a character (default value supplied). See `plot`.
`ylab`	a character (default value supplied). See `plot`.
`main`	a character (default value supplied). See `plot`.
`pch`	the type of "points" displayed by `jsd`. See `plot`.
`...`	additional arguments passed to `plot` by `jsd` and to respective generic methods by `contour.jpsth`, `image.jpsth` and `persp.jpsth`.

Details

The joint scatter diagram was introduced by Gerstein and Perkel (1972). The joint peristimulus time histogram is a binned version of it (Aertsen et al, 1989). jpsth2df allows the reformating of a jpsth object in order to compute a smooth version of it with gssanova, gssanova0 or gam.

Value

jsd is used for its side effect, a plot is generated and nothing is returned.

jpsth2df returns a data.frame with the following variables: Count, the counts per cell; X, the position of the cell on the X axis; Y, the position of the cell on the Y axis; and attributes: xBreaks, yBreaks, xTotal, yTotal, nbTrials, acquisitionWindow corresponding to the components of its argument with the same name and originalCall corresponding to component call.

jpsth returns a list of class jpsth with the following components:

`counts`	a matrix storing the counts per cell.
`density`	a matrix storing the density in each cell.
`xMids`	a vector containing the X positions of the cells.
`yMids`	a vector containing the Y positions of the cells.
`xBreaks`	a vector containing the bin boundaries of the cells along the X axis.
`yBreaks`	a vector containing the bin boundaries of the cells along the X axis.
`xTotal`	the total number of spikes of the "X" neuron.
`yTotal`	the total number of spikes of the "Y" neuron.
`xFreq`	the mean freqency of the "X" neuron.
`yFreq`	the mean freqency of the "Y" neuron.
`nbTrials`	the number of trials of `xRT` (and `yRT`).
`acquisitionWindow`	the boundaries of the acquisition window.
`call`	the matched call.

Note

I use "joint scatter diagram" for what Gerstein and Perkel (1972) more properly call a "joint peristimulus time scatter diagram".

Author(s)

Christophe Pouzat christophe.pouzat@gmail.com

References

Gerstein, G. L. and Perkel, D. H. (1972) Mutual temporal relationships among neuronal spike trains. Statistical techniques for display and analysis. Biophys J 12: 453–473. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1484144

Aertsen, A. M., Gerstein, G. L., Habib, M. K., Palm, G. (1989) Dynamics of neuronal firing correlation: modulation of "effective connectivity". J Neurophysiol 61: 900–917. http://jn.physiology.org/cgi/content/abstract/61/5/900

Examples

## load e070528citronellal data
data(e070528citronellal)
## plot a jsd with neuron 1 on X and neuron 2 on Y
jsd(e070528citronellal[[1]],e070528citronellal[[2]])
## now make the jpsth
j1.2 <- jpsth(e070528citronellal[[1]],e070528citronellal[[2]])
## make a contour plot
contour(j1.2)
## make an image plot
image(j1.2)
## make a persp plot
persp(j1.2)
## Not run: 
## fit a gss model with interactions
## use a larger bin width for the jpsth
j1.2 <- jpsth(e070528citronellal[[1]],e070528citronellal[[2]],0.2,0.2)
## get a data frame
j1.2DF <- jpsth2df(j1.2)
## To save computation time start analyzing
## just before the stimulation time
j1.2DF <- j1.2DF[j1.2DF$X > 6 & j1.2DF$Y>6,]
gf <- gssanova(Count ~ X*Y, family="poisson", data=j1.2DF,seed=20061001)
## Use the project function of gss to check the significance
## of the interaction term
project(gf2,inc=c("X","Y"))

## End(Not run)
## Not run: 
## fit a gam model assuming no interaction
## get a data frame
j1.2DF <- jpsth2df(j1.2)
fitNoI <- gam(Count ~ s(X,k=100,bs="cr") + s(Y,k=100,bs="cr"),data=j1.2DF,family=poisson())

## End(Not run)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(STAR)
Loading required package: survival
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-12. For overview type 'help("mgcv-package")'.
Loading required package: R2HTML
Loading required package: gss
Loading required package: codetools
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/STAR/jpsth.Rd_%03d_medium.png", width=480, height=480)
> ### Name: jpsth
> ### Title: Related Functions and Methods for Joint-PSTHs and Joint Scatter
> ###   Diagrams
> ### Aliases: jpsth jsd contour.jpsth image.jpsth persp.jpsth jpsth2df
> ### Keywords: models
> 
> ### ** Examples
> 
> ## load e070528citronellal data
> data(e070528citronellal)
> ## plot a jsd with neuron 1 on X and neuron 2 on Y
> jsd(e070528citronellal[[1]],e070528citronellal[[2]])
> ## now make the jpsth
> j1.2 <- jpsth(e070528citronellal[[1]],e070528citronellal[[2]])
> ## make a contour plot
> contour(j1.2)
> ## make an image plot
> image(j1.2)
> ## make a persp plot
> persp(j1.2)
> ## Not run: 
> ##D ## fit a gss model with interactions
> ##D ## use a larger bin width for the jpsth
> ##D j1.2 <- jpsth(e070528citronellal[[1]],e070528citronellal[[2]],0.2,0.2)
> ##D ## get a data frame
> ##D j1.2DF <- jpsth2df(j1.2)
> ##D ## To save computation time start analyzing
> ##D ## just before the stimulation time
> ##D j1.2DF <- j1.2DF[j1.2DF$X > 6 & j1.2DF$Y>6,]
> ##D gf <- gssanova(Count ~ X*Y, family="poisson", data=j1.2DF,seed=20061001)
> ##D ## Use the project function of gss to check the significance
> ##D ## of the interaction term
> ##D project(gf2,inc=c("X","Y"))
> ## End(Not run)
> ## Not run: 
> ##D ## fit a gam model assuming no interaction
> ##D ## get a data frame
> ##D j1.2DF <- jpsth2df(j1.2)
> ##D fitNoI <- gam(Count ~ s(X,k=100,bs="cr") + s(Y,k=100,bs="cr"),data=j1.2DF,family=poisson())
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>