STAR (version 0.3-7)

brt4df: Get Backward Recurrence Times from Data Frames Generated by mkGLMdf

Description

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.

Usage

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

Arguments

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.

Value

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

Details

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

References

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

See Also

mkGLMdf, data.frame, glm, mgcv

Examples

## 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)