lockedTrain: Construct and Plot Time-Dependent Cross-correlation Diagram

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/lockedTrain.R

Description

lockedTrain constructs and plot.lockedTrain (and print.lockedTrain) plot what van Stokkum et al (1986) call a time-dependent cross-correlation diagram. The lags between spikes of a test and a reference trains are plotted against the time of occurrence or the rank of the reference train spikes.

Usage

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lockedTrain(stRef, stTest, laglim, acquisitionWindow)
## S3 method for class 'lockedTrain'
plot(x, keepTime = FALSE,
            stimTimeCourse = NULL, colStim = "grey80",
            xlim, pch, xlab, ylab, main, ...)
## S3 method for class 'lockedTrain'
print(x,...)

Arguments

stRef

a spikeTrain or a repeatedTrain object.

stTest

a spikeTrain or a repeatedTrain object. If missing(stTest) is TRUE then stRef is used.

x

a lockedTrain object.

laglim

a two elements vector, the time window (in s) in which spikes in stTest around spikes in stRef are looked for. Default value are supplied when the argument is missing (+/- 3 times the sd of the inter-spike intervals of stRef).

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.

keepTime

a logical, if TRUE the ordinate is shown in s, otherwise (default) the spike index is shown.

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.

xlim

a numeric (default value supplied). See plot.

pch

data symbol used for the spikes. 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.

...

see plot or print.

Details

The time-dependent cross-correlation diagram is described in van Stokkum et al (1986) and is also used by Brillinger (1992) Fig. 4. For each spike of stRef neighboring spikes of stTest are selected within a window defined by laglim. The lag between these stTest spikes and the ones of stRef are displayed (that is, the times of the stRef spikes is subtracted from the times of the neighboring spikes in stTest).

If repeatedTrains are given for stRef and stTest they must have the same number of components and are interpreted as coming from repetitions of the same stimulation, the spike times of the different trains of stRef are therefore reordered.

The ordinate on the plot generated by plot.lockedTrain can be in term of real time or in term of stRef spike indexes.

If stimTimeCourse is specified a box corresponding to the stimulus presentation is drawn in the background.

Value

lockedTrain returns a LIST of class lockedTrain with the following components:

shiftedT

a list of lists. Each sublist has three components: refTime, the time of the reference spike; repIdx, the index of the stimulus repeat to which the reference spike belongs; crossTime, a vector of shifted times of the test neurons. These times are shifted because they are expressed with respect to the reference spike time.

nbRefSpikes

the total number of reference spikes used.

nbTestSpikes

the total number of test spikes occurring during the same observation period.

laglim

the value of laglim used.

acquisitionWindow

the value of the acquisitionWindow used.

obsTime

the total observation time used (in s).

call

the matched call.

plot.lockedTrain and print.lockedTrain are used for their side effects: a plot is generated. print.lockedTrain calls plot.lockedTrain.

Note

plot.lockedTrain displays essentially the "raw data" from which a cross-intensity histogram is built.

Author(s)

Christophe Pouzat [email protected]

References

van Stokkum, I. H., Johannesma, P. I. and Eggermont, J. J. (1986) Representation of time-dependent correlation and recurrence time functions. A new method to analyse non-stationary point processes. Biol Cybern 55: 17–24.

Brillinger, David R. (1992) Nerve Cell Spike Train Data Analysis: A Progression of Technique. JASA 87: 260–271.

See Also

as.spikeTrain, as.repeatedTrain, raster

Examples

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## Not run: 
## load spontaneous data of 4 putative projection neurons
## simultaneously recorded from the cockroach (Periplaneta
## americana) antennal lobe
data(CAL1S)
## convert data into spikeTrain objects
CAL1S <- lapply(CAL1S,as.spikeTrain)
## look at the individual trains
## first the "raw" data
CAL1S[["neuron 1"]]
## contruct the lockedTrain of each neuron with itself and look at
## it using a lag of +/- 25 ms
lockedTrain(CAL1S[["neuron 1"]],laglim=c(-1,1)*0.025)
lockedTrain(CAL1S[["neuron 2"]],laglim=c(-1,1)*0.025)
lockedTrain(CAL1S[["neuron 3"]],laglim=c(-1,1)*0.025)
lockedTrain(CAL1S[["neuron 4"]],laglim=c(-1,1)*0.025)

## Look at the Vanillin responses
## Get the data
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")
plot(CAL1V[["neuron 2"]],stimTimeCourse=c(4.49,4.99),main="N2")
plot(CAL1V[["neuron 3"]],stimTimeCourse=c(4.49,4.99),main="N3")
plot(CAL1V[["neuron 4"]],stimTimeCourse=c(4.49,4.99),main="N4")
## construct the locked train for the 3 pairs with neuron 1 as a
## reference
plot(lockedTrain(CAL1V[["neuron 1"]],CAL1V[["neuron 3"]],
     laglim=0.01*c(-1,1)),stimTimeCourse=c(4.49,4.99),pch="*")
plot(lockedTrain(CAL1V[["neuron 1"]],CAL1V[["neuron 2"]],
     laglim=0.01*c(-1,1)),stimTimeCourse=c(4.49,4.99),pch="*")
plot(lockedTrain(CAL1V[["neuron 1"]],CAL1V[["neuron 4"]],
     laglim=0.01*c(-1,1)),stimTimeCourse=c(4.49,4.99),pch="*")

## End(Not run)

STAR documentation built on May 31, 2017, 2:28 a.m.