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## networkspikes.R --- identify and analsyse network spikes
## Author: Stephen J Eglen
## Copyright: GPL
## Sun 28 Jan 2007
## Taking ideas from Eytan & Marom J Neurosci (2006).
##ns.T = 0.003 #bin time for network spikes
##ns.N = 10 #number of active electrodes.
## 2007-07-27: Code merged in from second version, temp in
## ~/proj/sangermea/test_ns.R
compute.ns <- function(s, ns.T, ns.N, sur, plot=FALSE) {
## Main entrance function to compute network spikes.
## Typical values:
## ns.T: 3
## ns.N: 10
## sur: 100
counts <- spikes.to.count2(s$spikes, time.interval=ns.T)
p <- find.peaks(counts, ns.N)
ns <- list(counts=counts, ns.N=ns.N, ns.T=ns.T)
class(ns) <- "ns"
m <- mean.ns(ns, p, plot=plot, nrow=4, ncol=4, ask=FALSE, sur=sur)
if (is.null(m)) {
## No network spikes found.
ns$brief <- c(n=0, peak.m=NA, peak.sd=NA, durn.m=NA, durn.sd=NA)
} else {
ns$mean <- m$ns.mean; ns$measures <- m$measures
peak.val <- ns$measures[,"peak.val"]
durn <- ns$measures[,"durn"]
ns$brief <- c(n=nrow(ns$measures),
peak.m=mean(peak.val), peak.sd=sd(peak.val),
durn.m=mean(durn, na.rm=TRUE), durn.sd=sd(durn, na.rm=TRUE))
}
ns
}
spikes.to.count2 <- function(spikes,
time.interval=1, #time bin of 1sec.
beg=floor(min(unlist(spikes))),
end=ceiling(max(unlist(spikes)))
)
{
## Convert the spikes for each cell into a firing rate (in Hz)
## We count the number of spikes within time bins of duration
## time.interval (measured in seconds).
##
## Currently cannot specify BEG or END as less than the
## range of spike times else you get an error from hist(). The
## default anyway is to do all the spikes within a data file.
##
## C version, which should replace spikes.to.count
## Returns a time series object.
## Each bin is of the form [t, t+dt) I believe, as shown by:
## spikes.to.count2(list( c(0, 6.9), c( 2, 4)))
## time.breaks <- seq(from=beg, to=end, by=time.interval)
nbins <- ceiling( (end-beg) / time.interval)
nspikes <- sapply(spikes, length) #already computed elsewhere!
z <- .C("ns_count_activity",
as.double(unlist(spikes)),
as.integer(nspikes),
as.integer(length(nspikes)),
as.double(beg), as.double(end), as.double(time.interval),
as.integer(nbins),
counts = integer(nbins),
PACKAGE="sjemea")
## Return counts as a time series.
res <- ts(data=z$counts, start=beg, deltat=time.interval)
res
}
plot.ns <- function(ns, ...) {
## Plot function for "ns" class.
plot(ns$counts, ...)
abline(h=ns$ns.N, col='red')
##peak.times <- times[ ns$peaks[,1]]
peak.times <- ns$measures[,"time"]
peak.val <- ns$measures[,"peak.val"]
points(peak.times, peak.val, col='blue', pch=19)
}
summary.ns <- function(ns) {
## Summary function for "ns" class.
n <- ns$brief["n"]
cat(sprintf("%d network spikes\n", n))
peak.m <- ns$brief["peak.m"]
peak.sd <- ns$brief["peak.sd"]
durn.m <- ns$brief["durn.m"]
durn.sd <- ns$brief["durn.sd"]
cat(sprintf("recruitment %.2f +/- %.2f\n", peak.m, peak.sd))
cat(sprintf("FWHM %.3f +/- %.3f (s)\n", durn.m, durn.sd))
}
mean.ns <- function(ns, p, sur=100,
plot=TRUE, nrow=8, ncol=8, ask=FALSE) {
## Compute the mean network spikes, and optionally show the
## individual network spikes.
## This code does not check to worry if there is a spike right at either
## end of the recording. naughty!
if (is.null(p)) {
if (is.null(ns$measures)) {
cat("*** No network spikes found\n")
return (NULL)
} else {
## use info previously stored in measures.
p <- ns$measures
}
}
if (plot) {
old.par <- par(mfrow=c(nrow,ncol), mar=c(2.5,1,1,1),ask=ask)
}
ave = rep(0, (2*sur)+1)
npts = length(ns$counts)
times <- time(ns$counts)
measures = matrix(NA, nrow=nrow(p), ncol=4)
colnames(measures) = c("time", "index", "peak.val", "durn")
n.ns = 0 #Number of valid network spikes found
for (i in 1:nrow(p)) {
peak.i = p[i,"index"]; lo = (peak.i-sur); hi = peak.i+sur
## Check that enough data can be found:
if ( (lo >0) && ( hi < npts) ) {
n.ns = n.ns + 1
dat = ns$counts[lo:hi]
peak.val = dat[sur+1]
measures[n.ns, "time"] = times[peak.i]
measures[n.ns, "index"] = peak.i
measures[n.ns, "peak.val"] = peak.val
if (plot) {
plot(dat, xaxt='n', yaxt='n', ylim=c(0,60),
bty='n', type='l',xlab='', ylab='')
##abline(v=sur+1)
max.time <- ns$ns.T * sur
axis(1, at=c(0,1,2)*sur,
##labels=c('-300 ms', '0 ms', '+300 ms'))
labels=c(-max.time, 0, max.time))
}
hm = find.halfmax(dat, peak.n=sur+1, frac=0.5, plot=plot)
measures[n.ns, "durn"] = hm$durn* ns$ns.T
if (plot) {
text <- sprintf("%d durn %.3f",
round(peak.val), measures[n.ns, "durn"])
legend("topleft", text, bty='n')
}
##dat2 = dat;
##dat2[1:(hm$xl-1)] = 0;
##dat2[(hm$xr+1):((2*sur)+1)] = 0;
##k = kurtosis(dat2)
##measures[n.ns, 1] = k
ave = ave + dat
}
}
if (n.ns < nrow(p)) {
## Some peaks could not be averaged, since they were at either
## beg/end of the recording.
## So, in this case, truncate the matrix of results to correct
## number of rows.
measures = measures[1:n.ns,,drop=FALSE]
}
## now show the average
if (n.ns > 0) {
ave = ave/n.ns
if (plot) {
plot(ave, xaxt='n', yaxt='n', bty='n', type='l',xlab='', ylab='')
legend("topleft", paste("m", round(max(ave))), bty='n')
find.halfmax(ave)
}
##stripchart(measures[,1], ylab='K', method='jitter', vert=T, pch=19,
##main=paste('kurtosis', round(mean(measures[,1]),3)))
if (plot) {
stripchart(measures[,"durn"], ylab='durn (s)', method='jitter',
vert=TRUE, pch=19,
main=paste('FWHM durn', round(mean(measures[,"durn"]),3)))
}
if (plot) {
par(old.par)
}
}
ns.mean = ts(ave, start=(-sur*ns$ns.T), deltat=ns$ns.T)
list(measures=measures, ns.mean=ns.mean)
}
show.ns <- function(ns, ...) {
## Show the individual network spikes after they have been computed.
##
## This is useful if you don't show the individual network spikes
## when they are first iterated over to calculate the mean.
res <- mean.ns(ns, p=NULL, ...)
NULL #ignore result
}
find.peaks <- function(trace, ns.N) {
## Peaks are defined as being all elements between two zero entries
## (one at start, one at end) in the time series. An alternate
## definiton might be to require some number N of consecutive zero
## entries to surround a peak.
max.peaks = 200000
npts = length(trace)
peaks = matrix(NA, nrow=max.peaks, ncol=2)
colnames(peaks) <- c("index", "peak.val")
n = 0
inside = FALSE;
for (i in 1:npts) {
cur = trace[i]
if(inside) {
## currently in a peak.
if (cur == 0) {
## no longer inside a peak, save results if peak was tall enough.
inside=FALSE;
if (peak > ns.N) {
n=n+1
if (n > max.peaks) {
## oh oh, need more room.
browser()
} else {
peaks[n,] = c(peak.t, peak)
}
}
} else {
## still in a peak
if (cur > peak) {
peak = cur; peak.t = i;
}
}
} else {
## currently outside a peak.
if (cur > 0) {
inside = TRUE; peak = cur; peak.t = i
}
}
}
## tidy up result at end.
if (n > 0) {
peaks = peaks[1:n,,drop=FALSE]
} else {
## no peaks found.
peaks = NULL
}
}
find.halfmax.cute <- function(y) {
## Given a peak within DAT, find the FWHM.
## This is a cute method, but not robust enough -- since it assumes
## that the peak is unimodel -- which may not be the case.
x = 1:length(y)
p.t = 101 #HACK!!!
half.max = y[p.t]/2 #HACK!!!
f <- approxfun(x, y)
f2 <- function(x) { f(x) - half.max }
l <- uniroot(f2, lower=1, upper=p.t)
r <- uniroot(f2, lower=p.t, upper=length(y))
segments(l$root, f(l$root), r$root, f(r$root), col='blue')
}
find.halfmax <- function(y, peak.n=NULL, plot=TRUE, frac=0.5) {
## Given a peak somwhere with Y, find the FWHM.
##
## If PEAK.N is not null, it will be location of the peak -- this is helpful
## when there are multiple peaks within one window, and we want to find
## the FWHM of the non-major peak.
## By default, frac = 0.5, to find the half max. Change this to some other
## value, e.g. 10% to find 10% onset and offset.
##
##
## This may fail for a few reasons, e.g. not finding half-max values within
## the range, running out of data...
## all of which should be counted for!
n = length(y)
if (is.null(peak.n))
peak.n = which.max(y)
peak.val = y[peak.n]
half.max = peak.val * frac
## Break the data into three segments:
## llllllllllllllllllPrrrrrrrrrrrrrrrrr
## P is the peak; examine curve to the left (lll) and to the right (rrr) to
## find when the peak has decayed to half max.
left.y = y[1:(peak.n-1)]
right.y = y[(peak.n+1):n]
## When finding the halfmax value in the left and right side, we
## have to check that first all of the halfmax value can be found.
## e.g. if the peak value is 50 and all values to the left are 45,
## there is no value to the left which is under 25, and so the half
## max value cannot be computed.
## Assume the half max point can be found, we interpolate to find
## the point, see below.
underhalf.l = which(left.y < half.max)
if ( any(underhalf.l) ) {
xl1 = underhalf.l[length(underhalf.l)] #get last point under halfmax.
xl2 = xl1+1
yl1 = y[xl1]; yl2 = y[xl2]
dy = half.max - yl1
## see picture.
## below, (xl2 - xl1) should equal 1.
dx = (dy *(xl2-xl1))/ (yl2-yl1)
xl.half = xl1 + dx
} else {
xl.half = NA # could not find half-max to left.
}
## Now work on right of curve. find first point at which y falls below
## half max value.
underhalf.r = which(right.y < half.max)
if ( any(underhalf.r) ) {
xr2 = underhalf.r[1] + peak.n
xr1 = xr2 - 1
yr1 = y[xr1]; yr2 = y[xr2]
dy = half.max - yr2
dx = dy * (xr1-xr2)/(yr1-yr2)
##stopifnot(dx<0)
xr.half = xr2 + dx
} else {
xr.half = NA
}
if(plot) {
##abline(v=xl.half, col='green'); abline(v=xr.half, col='green'); #temp
abline(h=peak.val * frac, col='red')
if (! any(is.na(c(xl.half, xr.half)))) {
## check first that both half-maxes are valid.
segments(xl.half, half.max, xr.half, half.max, col='blue')
}
}
list(xl=xl.half, xr=xr.half, durn=xr.half-xl.half)
}
## now interpolate -- hard way
## a <- approx(x, y, n=length(y)*30)
## lines(a)
## amax.x = which.max(a$y)
## points(a$x[amax.x], a$y[amax.x], col='blue', pch=19)
## ## find right side down.
## half.max = max(y)/2
## rx <- which(a$y[-(1:amax.x)]< half.max)[1] + amax.x
## ## find left side down.
## lx <- which(a$y[1:amax.x]< half.max)
## lx <- lx[length(lx)]
## segments(a$x[lx], a$y[lx], a$x[rx], a$y[rx], col='blue')
## The "R" way of interpolating -- nice!
check.ns.plot <- function(counts, p, xlim, ns.N) {
plot(counts$times, counts$sum, type='l', xlim=xlim,
xlab="time (s)", ylab='num active channels')
points(counts$times[p[,1]], p[,2], pch=19, col='blue')
abline(h=ns.N, col='red') #threshold line.
}
ns.bin.peak <- function(p, nbins=12, wid=5) {
## Bin values in P into a set of NBINS bins, of size WID.
## Bins are right-closed (except for first bin, closed at both ends).
## Labels are added onto the bins.
##
## x <- c(0, 4,5, 20, 54,55, 60)
## ns.bin.peak(x, wid=10, nbins=7 )
##
if ( is.null(p) ) {
## no valid values, so no need to make the histogram.
## This happens when there are no network spikes.
p <- 0; invalid <- TRUE
} else {
invalid <- FALSE
}
b <- seq(from=0, by=wid, length=nbins+1)
max.allowed <- max(b)
if ( any( above <- which(p > max.allowed)) ) {
stop("some values above max.allowed")
}
h <- hist(p, plot=FALSE, breaks=b)
c <- h$counts
if (invalid) {
## no valid counts, so set all counts to zero.
c <- c*0
}
l <- hist.make.labels(0, max.allowed, nbins)
names(c) <- l
c
}
ns.identity <- function(s, w=0.1) {
## Return the "NSID" matrix, Network Spike IDentity.
## Which channels contributed to which network spikes?
## W is window of spike identity, +/- 0.1s by default.
## peak.times here should be the middle of the NS bin.
peak.times <- s$ns$measures[,"time"] + (s$ns$ns.T/2)
## We do the transpose here so that one row is one network spike.
nsid <- t(ns.coincident(peak.times, s$spikes, w))
}
ns.coincident <- function(a, bs, w) {
## A is a vector of reference times, sorted, lowest first. (Here
## the times of the peaks of the network spikes.)
## B is a list of vectors of spike times. (Each vector of spike
## times is sorted, lowest first.)
## For each spike train in B, we see if there was a spike within a
## time window +/- W of the time of each event in A. If there was a
## "close" spike in spike train j from B to event i , then
## MAT[j,i]=1.
## (MAT is a matrix of size CxN, where C is the number of spike
## trains in BS, and N is the number of events in A.)
## MAT is transposed by the higher level function -- it is kept this
## way for ease of the C implementation.
spike.lens <- sapply(bs, length)
num.channels <- length(spike.lens)
z <- .C("coincident_arr", as.double(a), as.integer(length(a)),
as.double(unlist(bs)), as.integer(spike.lens),
as.integer(num.channels),
close = integer(length(a)*num.channels),
as.double(w), PACKAGE="sjemea")
mat <- matrix(z$close, nrow=num.channels, byrow=TRUE)
dimnames(mat) <- list(channel=1:num.channels, ns.peak=a)
mat
}
######################################################################
## End of functions
######################################################################
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