When constructing peri-event time histograms and raster-plots it's often necessary to repeatedly do the following operation:
find(X > T1 & X <= T2)
where X is a vector of the times of some measurements (eg a spike train) and T1 and T2 are the left and right edges of an event-aligned window (eg stimulus onset).
While find() is generally fast, it can get quite slow for large numbers of events/trials because it is necessary to loop over the vector of alignment-events. findInRange() uses mex to achieve much greater speeds:

X = rand(1e6,1)*1e6;
T1 = rand(1e3,1)*1e3;
T2 = T1 + 5;

tic;
V1 = findInRange(X, T1, T2);
time1 = toc;

tic;
for i=1:length(T1)
V2{i} = X(X > T1(i) & X <= T2(i));
end
time2 = toc;

time2/time1

ans =

91.3441