brt4df: Get Backward Recurrence Times from Data Frames Generated by...
In STAR: Spike Train Analysis with R

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

Spike trains discharge models for single neurons are rarely renewal. They require more information than just the elapsed time since the last spike. Function brt4df generates this additional information from a data frame obtained by mkGLMdf.

1 2	brt4df(df, varName, max.order = 1, colNames, auto = TRUE, normalise = function(x) as.numeric(scale(log(x))))

`df`	A `data.frame` generated by `mkGLMdf` and containing the events of a single neuron.
`varName`	The name of one of the variables of `df`. It should be one of the "elapsed time" variables, like, `lN.x`, where `x` stands for a neuron number.
`max.order`	How many events should looked for in the past?
`colNames`	Names of the columns of the returned `data.frame`. If missing default names are provided.
`auto`	A logical. Does `varName` refer to the elapsed times since the last spike of the neuron whose spikes are recorded in the `event` variable (`TRUE`) or not (`FALSE`)?
`normalise`	A `function` applied to the extracted data in order to normalise them. If missing ,nothing is done and the extracted data are left unchanged.

If the spike required to evaluate the elapsed time is not contained in df then NA will be the reported elapsed time.

A data.frame is returned with as many variable as max.order and as many rows as df.

Christophe Pouzat christophe.pouzat@gmail.com

Kass, Robert E. and Ventura, Val\'erie (2001) A spike-train probability model Neural Comput. 13: 1713–1720.

Truccolo, W., Eden, U. T., Fellows, M. R., Donoghue, J. P. and Brown, E. N. (2005) A Point Process Framework for Relating Neural Spiking Activity to Spiking History, Neural Ensemble and Extrinsic Covariate Effects J Neurophysiol 93: 1074–1089. http://jn.physiology.org/cgi/content/abstract/93/2/1074

mkGLMdf, data.frame, glm, mgcv

## Not run: 
## Let us consider neuron 1 of the CAL2S data set
data(CAL2S)
CAL2S <- lapply(CAL2S,as.spikeTrain)
CAL2S[["neuron 1"]]
renewalTestPlot(CAL2S[["neuron 1"]])
summary(CAL2S[["neuron 1"]])
## Make a data frame with a 4 ms time resolution
cal2Sdf <- mkGLMdf(CAL2S,0.004,0,60)
## keep the part relative to neuron 1
n1.cal2sDF <- cal2Sdf[cal2Sdf$neuron=="1",]
## remove unnecessary data
rm(cal2Sdf)
## Extract the elapsed time since the second to last and
## third to last for neuron 1. Normalise the result. 
n1.cal2sDF[c("rlN.1","rsN.1","rtN.1")] <- brt4df(n1.cal2sDF,"lN.1",2,c("rlN.1","rsN.1","rtN.1"))
## load mgcv library
library(mgcv)
## fit a model with a tensorial product involving the last
## three spikes and using a cubic spline basis for the last two
n1S.fitA <- gam(event ~ te(rlN.1,rsN.1,bs="cr") + rtN.1,data=n1.cal2sDF,family=binomial(link="logit"))
summary(n1S.fitA)
## plot the result in 2 different ways
plot(n1S.fitA)
vis.gam(n1S.fitA,phi=20,theta=45)


## End(Not run)