Spike Trains of several Cockroach Antennal Lobe Neurons Recorded from Six Animals

Description

Four (CAL1S and CAL1V), three (CAL2S and CAL2C), three (e060517spont and e060517ionon), three (e060817spont, e060817terpi, e060817citron and e060817mix), two (e060824spont and e060824citral) and four (e070528spont and e070528citronellal) Cockroach (Periplaneta americana) antennal lobe neurons (putative projection neurons) were recorded simultaneously and extracellularly during spontaneous activity and odors (vanilin, citral, citronellal, terpineol, beta-ionon) responses from six different animals. The data sets contain the sorted spike trains of the neurons.

Usage

data(CAL1S)
data(CAL1V)
data(CAL2S)
data(CAL2C)
data(e060517spont)
data(e060517ionon)
data(e060817spont)
data(e060817terpi)
data(e060817citron)
data(e060817mix)
data(e060824spont)
data(e060824citral)
data(e070528spont)
data(e070528citronellal)

Format

CAL1S is a named list with 4 components ("neuron 1", "neuron 2", "neuron 3", "neuron 4"). Each component contains the spike train (ie, action potentials occurrence times) of one neuron recorded during 30 s of spontaneous activity. Times are expressed in seconds.

CAL1V is a named list with 4 components ("neuron 1", "neuron 2", "neuron 3", "neuron 4"). Each component is a named list with 20 components: "stim. 1", ..., "stim. 20". Each sub-list contains the spike train of one neuron during 1 stimulation (odor puff) with vanillin (http://en.wikipedia.org/wiki/Vanillin). Each acquisition was 10 s long. The command to the odor delivery valve was on between sec 4.49 and sec 4.99.

CAL2S is a named list with 3 components ("neuron 1", "neuron 2", "neuron 3"). Each component contains the spike train (ie, action potentials occurrence times) of one neuron recorded during 1 mn of spontaneous activity. Times are expressed in seconds.

CAL2C is a named list with 3 components ("neuron 1", "neuron 2", "neuron 3"). Each component is a named list with 20 components: "stim. 1", ..., "stim. 20". Each sub-list contains the spike train of one neuron during 1 stimulation (odor puff) with citral (http://en.wikipedia.org/wiki/Citral). Each acquisition was 14 s long. The command to the odor delivery valve was on between sec 5.87 and sec 6.37.

e060517spont is a named list of with 3 components ("neuron 1", "neuron 2", "neuron 3"). Each component is a spikeTrain object (ie, action potentials occurrence times) of one neuron recorded during 61 s of spontaneous activity. Times are expressed in seconds.

e060517ionon is a named list with 3 components ("neuron 1", "neuron 2", "neuron 3"). Each component is a repeatedTrain object with 19 spikeTrain objects: "stim. 1", ..., "stim. 19". Each spikeTrain contains the spike train of one neuron during 1 stimulation (odor puff) with beta-ionon (http://commons.wikimedia.org/wiki/Image:Beta-Ionon.svg). Each acquisition was 15 s long. The command to the odor delivery valve was on between sec 6.07 and sec 6.57.

e060817spont is a named list of with 3 components ("neuron 1", "neuron 2", "neuron 3"). Each component is a spikeTrain object (ie, action potentials occurrence times) of one neuron recorded during 60 s of spontaneous activity. Times are expressed in seconds.

e060817terpi is a named list with 3 components ("neuron 1", "neuron 2", "neuron 3"). Each component is a repeatedTrain object with 20 spikeTrain objects: "stim. 1", ..., "stim. 20". Each spikeTrain contains the spike train of one neuron during 1 stimulation (odor puff) with terpineol (http://en.wikipedia.org/wiki/Terpineol). Each acquisition was 15 s long. The command to the odor delivery valve was on between sec 6.03 and sec 6.53.

e060817citron is a named list with 3 components ("neuron 1", "neuron 2", "neuron 3"). Each component is a repeatedTrain object with 20 spikeTrain objects: "stim. 1", ..., "stim. 20". Each spikeTrain contains the spike train of one neuron during 1 stimulation (odor puff) with citronellal (http://en.wikipedia.org/wiki/Citronellal). Each acquisition was 15 s long. The command to the odor delivery valve was on between sec 5.99 and sec 6.49.

e060817mix is a named list with 3 components ("neuron 1", "neuron 2", "neuron 3"). Each component is a repeatedTrain object with 20 spikeTrain objects: "stim. 1", ..., "stim. 20". Each spikeTrain contains the spike train of one neuron during 1 stimulation (odor puff) with a mixture of terpinaol and citronellal (the sum of the two previous stim.). Each acquisition was 15 s long. The command to the odor delivery valve was on between sec 6.01 and sec 6.51.

e060824spont is a named list of with 2 components ("neuron 1", "neuron 2"). Each component is a spikeTrain object (ie, action potentials occurrence times) of one neuron recorded during 59 s of spontaneous activity. Times are expressed in seconds.

e060824citral is a named list with 2 components ("neuron 1", "neuron 2"). Each component is a named list with 20 components: "stim. 1", ..., "stim. 20". Each sub-list contains the spike train of one neuron during 1 stimulation (odor puff) with citral (http://en.wikipedia.org/wiki/Citral). Each acquisition was 15 s long. The command to the odor delivery valve was on between sec 6.01 and sec 6.51.

e070528spont is a named list of with 4 components ("neuron 1", "neuron 2", "neuron 3", "neuron 4"). Each component is a spikeTrain object (ie, action potentials occurrence times) of one neuron recorded during 60 s of spontaneous activity. Times are expressed in seconds.

e070528citronellal is a named list with 4 components ("neuron 1", "neuron 2", "neuron 3", "neuron 4"). Each component is a repeatedTrain object with 15 spikeTrain objects: "stim. 1", ..., "stim. 15". Each spikeTrain contains the spike train of one neuron during 1 stimulation (odor puff) with citronellal (http://en.wikipedia.org/wiki/Citronellal). Each acquisition was 13 s long. The command to the odor delivery valve was on between sec 6.14 and sec 6.64.

Details

Every repeatedTrain object of these data sets has an attribute named stimTimeCourse containing the openng and closing times of the odor delivery valve.

The data were recorded from neighboring sites on a NeuroNexus (http://neuronexustech.com/) silicon probe. Sorting was done with SpikeOMatic with superposition resolution which can AND DOES lead to artifcats on cross-correlograms.

Source

Recording and spike sorting performed by Antoine Chaffiol antoine.chaffiol@univ-paris5.fr at the Cerebral Physiology Lab, CNRS UMR 8118: http://www.biomedicale.univ-paris5.fr/physcerv/physiologie_cerebrale.htm.

References

http://www.biomedicale.univ-paris5.fr/physcerv/C_Pouzat/Doc/ChaffiolEtAl_FENS2006.pdf

Examples

## load CAL1S data
data(CAL1S)
## convert the data into spikeTrain objects
CAL1S <- lapply(CAL1S,as.spikeTrain)
## look at the train of the 1sd neuron
CAL1S[["neuron 1"]]
## fit the 6 different renewal models to the 1st neuron spike train
compModels(CAL1S[["neuron 1"]])
## look at the ISI distribution with the fitted invgauss dist for
## this 1st neuron
isiHistFit(CAL1S[["neuron 1"]],model="invgauss")

## load CAL1V data
data(CAL1V)
## convert them to repeatedTrain objects
CAL1V <- lapply(CAL1V, as.repeatedTrain)
## look at the raster of the 1st neuron
CAL1V[["neuron 1"]]

## load e070528spont data
data(e070528spont)
## look at the spike train of the 1st neuron
e070528spont[["neuron 1"]]

## load e070528citronellal data
data(e070528citronellal)
## Get the stimulus time course
attr(e070528citronellal[["neuron 1"]],"stimTimeCourse")
## look at the raster of the 1st neuron
plot(e070528citronellal[["neuron 1"]],stim=c(6.14,6.64))

## Not run: 
## A "detailed" analysis of e060817 were 2 odors as well as there mixtures
## were used.
## Load the terpineol, citronellal and mixture response data
data(e060817terpi)
data(e060817citron)
data(e060817mix)
## get smooth psths with gsspsth0
e060817terpiN1PSTH <- gsspsth0(e060817terpi[["neuron 1"]])
e060817terpiN2PSTH <- gsspsth0(e060817terpi[["neuron 2"]])
e060817terpiN3PSTH <- gsspsth0(e060817terpi[["neuron 3"]])
e060817citronN1PSTH <- gsspsth0(e060817citron[["neuron 1"]])
e060817citronN2PSTH <- gsspsth0(e060817citron[["neuron 2"]])
e060817citronN3PSTH <- gsspsth0(e060817citron[["neuron 3"]])
e060817mixN1PSTH <- gsspsth0(e060817mix[["neuron 1"]])
e060817mixN2PSTH <- gsspsth0(e060817mix[["neuron 2"]])
e060817mixN3PSTH <- gsspsth0(e060817mix[["neuron 3"]])
## look at them
## Neuron 1
plot(e060817terpiN1PSTH,stimTimeCourse=attr(e060817terpi[["neuron 1"]],"stimTimeCourse"),colCI=2)
plot(e060817citronN1PSTH,stimTimeCourse=attr(e060817citron[["neuron 1"]],"stimTimeCourse"),colCI=2)
plot(e060817mixN1PSTH,stimTimeCourse=attr(e060817mix[["neuron 1"]],"stimTimeCourse"),colCI=2)
## Neuron 2
plot(e060817terpiN2PSTH,stimTimeCourse=attr(e060817terpi[["neuron 2"]],"stimTimeCourse"),colCI=2)
plot(e060817citronN2PSTH,stimTimeCourse=attr(e060817citron[["neuron 2"]],"stimTimeCourse"),colCI=2)
plot(e060817mixN2PSTH,stimTimeCourse=attr(e060817mix[["neuron 2"]],"stimTimeCourse"),colCI=2)
## Neuron 3
plot(e060817terpiN3PSTH,stimTimeCourse=attr(e060817terpi[["neuron 3"]],"stimTimeCourse"),colCI=2)
plot(e060817citronN3PSTH,stimTimeCourse=attr(e060817citron[["neuron 3"]],"stimTimeCourse"),colCI=2)
plot(e060817mixN3PSTH,stimTimeCourse=attr(e060817mix[["neuron 3"]],"stimTimeCourse"),colCI=2)

## Make now fancier plots with superposed psths ####
## Take into account the fact that the stimuli onsets are not identical

## Neuron 1
plot(e060817mixN1PSTH$mids-0.02,e060817mixN1PSTH$ciUp,type="n",ylim=c(0,max(e060817mixN1PSTH$ciUp)),xlim=c(5,14),xlab="Time (s)",ylab="Firing rate (Hz)",main="Neuron 1 e060817")
rect(5.99,0,6.49,max(e060817mixN1PSTH$ciUp),col="grey80",border=NA)
abline(h=0)
polygon(c(e060817mixN1PSTH$mids-0.02,rev(e060817mixN1PSTH$mids-0.02)),c(e060817mixN1PSTH$ciLow,rev(e060817mixN1PSTH$ciUp)),col=rgb(1,0,1,0.5),border=NA)
polygon(c(e060817citronN1PSTH$mids,rev(e060817citronN1PSTH$mids)),c(e060817citronN1PSTH$ciLow,rev(e060817citronN1PSTH$ciUp)),col=rgb(1,0,0,0.5),border=NA)
polygon(c(e060817terpiN1PSTH$mids-0.04,rev(e060817terpiN1PSTH$mids-0.04)),c(e060817terpiN1PSTH$ciLow,rev(e060817terpiN1PSTH$ciUp)),col=rgb(0,0,1,0.5),border=NA)
lines(e060817terpiN1PSTH$mids-0.04,e060817terpiN1PSTH$freq,col=rgb(0,0,1),lwd=2)
lines(e060817citronN1PSTH$mids,e060817citronN1PSTH$freq,col=rgb(1,0,0),lwd=2)
lines(e060817mixN1PSTH$mids-0.02,e060817mixN1PSTH$freq,col=rgb(0,0,0),lwd=2)
legend(8,0.9*max(e060817mixN1PSTH$ciUp),c("Terpineol","Citronellal","Mixture"),col=c(4,2,1),lwd=2)

## Neuron 2
plot(e060817mixN2PSTH$mids-0.02,e060817mixN2PSTH$ciUp,type="n",ylim=c(0,max(e060817mixN2PSTH$ciUp)),xlim=c(5,14),xlab="Time (s)",ylab="Firing rate (Hz)",main="Neuron 2 e060817")
rect(5.99,0,6.49,max(e060817mixN2PSTH$ciUp),col="grey80",border=NA)
abline(h=0)
polygon(c(e060817mixN2PSTH$mids-0.02,rev(e060817mixN2PSTH$mids-0.02)),c(e060817mixN2PSTH$ciLow,rev(e060817mixN2PSTH$ciUp)),col=rgb(1,0,1,0.5),border=NA)
polygon(c(e060817citronN2PSTH$mids,rev(e060817citronN2PSTH$mids)),c(e060817citronN2PSTH$ciLow,rev(e060817citronN2PSTH$ciUp)),col=rgb(1,0,0,0.5),border=NA)
polygon(c(e060817terpiN2PSTH$mids-0.04,rev(e060817terpiN2PSTH$mids-0.04)),c(e060817terpiN2PSTH$ciLow,rev(e060817terpiN2PSTH$ciUp)),col=rgb(0,0,1,0.5),border=NA)
lines(e060817terpiN2PSTH$mids-0.04,e060817terpiN2PSTH$freq,col=rgb(0,0,1),lwd=2)
lines(e060817citronN2PSTH$mids,e060817citronN2PSTH$freq,col=rgb(1,0,0),lwd=2)
lines(e060817mixN2PSTH$mids-0.02,e060817mixN2PSTH$freq,col=rgb(0,0,0),lwd=2)
legend(8,0.9*max(e060817mixN2PSTH$ciUp),c("Terpineol","Citronellal","Mixture"),col=c(4,2,1),lwd=2)

## Neuron 3
plot(e060817mixN3PSTH$mids-0.02,e060817mixN3PSTH$ciUp,type="n",ylim=c(0,max(e060817mixN3PSTH$ciUp)),xlim=c(5,14),xlab="Time (s)",ylab="Firing rate (Hz)",main="Neuron 3 e060817")
rect(5.99,0,6.49,max(e060817mixN3PSTH$ciUp),col="grey80",border=NA)
abline(h=0)
polygon(c(e060817mixN3PSTH$mids-0.02,rev(e060817mixN3PSTH$mids-0.02)),c(e060817mixN3PSTH$ciLow,rev(e060817mixN3PSTH$ciUp)),col=rgb(1,0,1,0.5),border=NA)
polygon(c(e060817citronN3PSTH$mids,rev(e060817citronN3PSTH$mids)),c(e060817citronN3PSTH$ciLow,rev(e060817citronN3PSTH$ciUp)),col=rgb(1,0,0,0.5),border=NA)
polygon(c(e060817terpiN3PSTH$mids-0.04,rev(e060817terpiN3PSTH$mids-0.04)),c(e060817terpiN3PSTH$ciLow,rev(e060817terpiN3PSTH$ciUp)),col=rgb(0,0,1,0.5),border=NA)
lines(e060817terpiN3PSTH$mids-0.04,e060817terpiN3PSTH$freq,col=rgb(0,0,1),lwd=2)
lines(e060817citronN3PSTH$mids,e060817citronN3PSTH$freq,col=rgb(1,0,0),lwd=2)
lines(e060817mixN3PSTH$mids-0.02,e060817mixN3PSTH$freq,col=rgb(0,0,0),lwd=2)
legend(8,0.9*max(e060817mixN3PSTH$ciUp),c("Terpineol","Citronellal","Mixture"),col=c(4,2,1),lwd=2)

## 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)

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Type 'license()' or 'licence()' for distribution details.

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> 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/cockroachALData.Rd_%03d_medium.png", width=480, height=480)
> ### Name: cockroachAlData
> ### Title: Spike Trains of several Cockroach Antennal Lobe Neurons Recorded
> ###   from Six Animals
> ### Aliases: CAL1S CAL1V CAL2S CAL2C e060517spont e060517ionon e060817spont
> ###   e060817terpi e060817citron e060817mix e060824spont e060824citral
> ###   e070528spont e070528citronellal
> ### Keywords: datasets
> 
> ### ** Examples
> 
> ## load CAL1S data
> data(CAL1S)
> ## convert the data into spikeTrain objects
> CAL1S <- lapply(CAL1S,as.spikeTrain)
> ## look at the train of the 1sd neuron
> CAL1S[["neuron 1"]]
> ## fit the 6 different renewal models to the 1st neuron spike train
> compModels(CAL1S[["neuron 1"]])
 invgauss     lnorm    llogis   weibull     gamma      rexp 
-477.1587 -448.4228 -444.7260 -387.0661 -364.5763 -348.6731 
Warning messages:
1: In rep(yi[si > 0], each = ni[si > 0]) :
  first element used of 'each' argument
2: In rep(yi[si > 0], each = si[si > 0]) :
  first element used of 'each' argument
3: In rep(yi[si > 0], each = si[si > 0]) :
  first element used of 'each' argument
4: In rep(yi[si > 0], each = si[si > 0]) :
  first element used of 'each' argument
5: In rep(yi[si > 0], each = si[si > 0]) :
  first element used of 'each' argument
6: In rep(yi[si > 0], each = si[si > 0]) :
  first element used of 'each' argument
7: In rep(yi[si > 0], each = si[si > 0]) :
  first element used of 'each' argument
> ## look at the ISI distribution with the fitted invgauss dist for
> ## this 1st neuron
> isiHistFit(CAL1S[["neuron 1"]],model="invgauss")
> 
> ## load CAL1V data
> data(CAL1V)
> ## convert them to repeatedTrain objects
> CAL1V <- lapply(CAL1V, as.repeatedTrain)
> ## look at the raster of the 1st neuron
> CAL1V[["neuron 1"]]
> 
> ## load e070528spont data
> data(e070528spont)
> ## look at the spike train of the 1st neuron
> e070528spont[["neuron 1"]]
> 
> ## load e070528citronellal data
> data(e070528citronellal)
> ## Get the stimulus time course
> attr(e070528citronellal[["neuron 1"]],"stimTimeCourse")
[1] 6.14 6.64
> ## look at the raster of the 1st neuron
> plot(e070528citronellal[["neuron 1"]],stim=c(6.14,6.64))
> 
> ## Not run: 
> ##D ## A "detailed" analysis of e060817 were 2 odors as well as there mixtures
> ##D ## were used.
> ##D ## Load the terpineol, citronellal and mixture response data
> ##D data(e060817terpi)
> ##D data(e060817citron)
> ##D data(e060817mix)
> ##D ## get smooth psths with gsspsth0
> ##D e060817terpiN1PSTH <- gsspsth0(e060817terpi[["neuron 1"]])
> ##D e060817terpiN2PSTH <- gsspsth0(e060817terpi[["neuron 2"]])
> ##D e060817terpiN3PSTH <- gsspsth0(e060817terpi[["neuron 3"]])
> ##D e060817citronN1PSTH <- gsspsth0(e060817citron[["neuron 1"]])
> ##D e060817citronN2PSTH <- gsspsth0(e060817citron[["neuron 2"]])
> ##D e060817citronN3PSTH <- gsspsth0(e060817citron[["neuron 3"]])
> ##D e060817mixN1PSTH <- gsspsth0(e060817mix[["neuron 1"]])
> ##D e060817mixN2PSTH <- gsspsth0(e060817mix[["neuron 2"]])
> ##D e060817mixN3PSTH <- gsspsth0(e060817mix[["neuron 3"]])
> ##D ## look at them
> ##D ## Neuron 1
> ##D plot(e060817terpiN1PSTH,stimTimeCourse=attr(e060817terpi[["neuron 1"]],"stimTimeCourse"),colCI=2)
> ##D plot(e060817citronN1PSTH,stimTimeCourse=attr(e060817citron[["neuron 1"]],"stimTimeCourse"),colCI=2)
> ##D plot(e060817mixN1PSTH,stimTimeCourse=attr(e060817mix[["neuron 1"]],"stimTimeCourse"),colCI=2)
> ##D ## Neuron 2
> ##D plot(e060817terpiN2PSTH,stimTimeCourse=attr(e060817terpi[["neuron 2"]],"stimTimeCourse"),colCI=2)
> ##D plot(e060817citronN2PSTH,stimTimeCourse=attr(e060817citron[["neuron 2"]],"stimTimeCourse"),colCI=2)
> ##D plot(e060817mixN2PSTH,stimTimeCourse=attr(e060817mix[["neuron 2"]],"stimTimeCourse"),colCI=2)
> ##D ## Neuron 3
> ##D plot(e060817terpiN3PSTH,stimTimeCourse=attr(e060817terpi[["neuron 3"]],"stimTimeCourse"),colCI=2)
> ##D plot(e060817citronN3PSTH,stimTimeCourse=attr(e060817citron[["neuron 3"]],"stimTimeCourse"),colCI=2)
> ##D plot(e060817mixN3PSTH,stimTimeCourse=attr(e060817mix[["neuron 3"]],"stimTimeCourse"),colCI=2)
> ##D 
> ##D ## Make now fancier plots with superposed psths ####
> ##D ## Take into account the fact that the stimuli onsets are not identical
> ##D 
> ##D ## Neuron 1
> ##D plot(e060817mixN1PSTH$mids-0.02,e060817mixN1PSTH$ciUp,type="n",ylim=c(0,max(e060817mixN1PSTH$ciUp)),xlim=c(5,14),xlab="Time (s)",ylab="Firing rate (Hz)",main="Neuron 1 e060817")
> ##D rect(5.99,0,6.49,max(e060817mixN1PSTH$ciUp),col="grey80",border=NA)
> ##D abline(h=0)
> ##D polygon(c(e060817mixN1PSTH$mids-0.02,rev(e060817mixN1PSTH$mids-0.02)),c(e060817mixN1PSTH$ciLow,rev(e060817mixN1PSTH$ciUp)),col=rgb(1,0,1,0.5),border=NA)
> ##D polygon(c(e060817citronN1PSTH$mids,rev(e060817citronN1PSTH$mids)),c(e060817citronN1PSTH$ciLow,rev(e060817citronN1PSTH$ciUp)),col=rgb(1,0,0,0.5),border=NA)
> ##D polygon(c(e060817terpiN1PSTH$mids-0.04,rev(e060817terpiN1PSTH$mids-0.04)),c(e060817terpiN1PSTH$ciLow,rev(e060817terpiN1PSTH$ciUp)),col=rgb(0,0,1,0.5),border=NA)
> ##D lines(e060817terpiN1PSTH$mids-0.04,e060817terpiN1PSTH$freq,col=rgb(0,0,1),lwd=2)
> ##D lines(e060817citronN1PSTH$mids,e060817citronN1PSTH$freq,col=rgb(1,0,0),lwd=2)
> ##D lines(e060817mixN1PSTH$mids-0.02,e060817mixN1PSTH$freq,col=rgb(0,0,0),lwd=2)
> ##D legend(8,0.9*max(e060817mixN1PSTH$ciUp),c("Terpineol","Citronellal","Mixture"),col=c(4,2,1),lwd=2)
> ##D 
> ##D ## Neuron 2
> ##D plot(e060817mixN2PSTH$mids-0.02,e060817mixN2PSTH$ciUp,type="n",ylim=c(0,max(e060817mixN2PSTH$ciUp)),xlim=c(5,14),xlab="Time (s)",ylab="Firing rate (Hz)",main="Neuron 2 e060817")
> ##D rect(5.99,0,6.49,max(e060817mixN2PSTH$ciUp),col="grey80",border=NA)
> ##D abline(h=0)
> ##D polygon(c(e060817mixN2PSTH$mids-0.02,rev(e060817mixN2PSTH$mids-0.02)),c(e060817mixN2PSTH$ciLow,rev(e060817mixN2PSTH$ciUp)),col=rgb(1,0,1,0.5),border=NA)
> ##D polygon(c(e060817citronN2PSTH$mids,rev(e060817citronN2PSTH$mids)),c(e060817citronN2PSTH$ciLow,rev(e060817citronN2PSTH$ciUp)),col=rgb(1,0,0,0.5),border=NA)
> ##D polygon(c(e060817terpiN2PSTH$mids-0.04,rev(e060817terpiN2PSTH$mids-0.04)),c(e060817terpiN2PSTH$ciLow,rev(e060817terpiN2PSTH$ciUp)),col=rgb(0,0,1,0.5),border=NA)
> ##D lines(e060817terpiN2PSTH$mids-0.04,e060817terpiN2PSTH$freq,col=rgb(0,0,1),lwd=2)
> ##D lines(e060817citronN2PSTH$mids,e060817citronN2PSTH$freq,col=rgb(1,0,0),lwd=2)
> ##D lines(e060817mixN2PSTH$mids-0.02,e060817mixN2PSTH$freq,col=rgb(0,0,0),lwd=2)
> ##D legend(8,0.9*max(e060817mixN2PSTH$ciUp),c("Terpineol","Citronellal","Mixture"),col=c(4,2,1),lwd=2)
> ##D 
> ##D ## Neuron 3
> ##D plot(e060817mixN3PSTH$mids-0.02,e060817mixN3PSTH$ciUp,type="n",ylim=c(0,max(e060817mixN3PSTH$ciUp)),xlim=c(5,14),xlab="Time (s)",ylab="Firing rate (Hz)",main="Neuron 3 e060817")
> ##D rect(5.99,0,6.49,max(e060817mixN3PSTH$ciUp),col="grey80",border=NA)
> ##D abline(h=0)
> ##D polygon(c(e060817mixN3PSTH$mids-0.02,rev(e060817mixN3PSTH$mids-0.02)),c(e060817mixN3PSTH$ciLow,rev(e060817mixN3PSTH$ciUp)),col=rgb(1,0,1,0.5),border=NA)
> ##D polygon(c(e060817citronN3PSTH$mids,rev(e060817citronN3PSTH$mids)),c(e060817citronN3PSTH$ciLow,rev(e060817citronN3PSTH$ciUp)),col=rgb(1,0,0,0.5),border=NA)
> ##D polygon(c(e060817terpiN3PSTH$mids-0.04,rev(e060817terpiN3PSTH$mids-0.04)),c(e060817terpiN3PSTH$ciLow,rev(e060817terpiN3PSTH$ciUp)),col=rgb(0,0,1,0.5),border=NA)
> ##D lines(e060817terpiN3PSTH$mids-0.04,e060817terpiN3PSTH$freq,col=rgb(0,0,1),lwd=2)
> ##D lines(e060817citronN3PSTH$mids,e060817citronN3PSTH$freq,col=rgb(1,0,0),lwd=2)
> ##D lines(e060817mixN3PSTH$mids-0.02,e060817mixN3PSTH$freq,col=rgb(0,0,0),lwd=2)
> ##D legend(8,0.9*max(e060817mixN3PSTH$ciUp),c("Terpineol","Citronellal","Mixture"),col=c(4,2,1),lwd=2)
> ## End(Not run)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>