There is a great statistic called "expectation density" which is related to
auto-correlation. I haven't used the statistic in a decade but I recall that
there is a great exposition of it by the originators, Poggio and Verenstein
(sp?). If I come across it, I'll let you know, but you should be able to dig
it up (it's old......30 years maybe.....or more). Also, if you hadn't done
so, make sure that whatever transformation(s) you choose, you compare it
with those computed on the same data that have been "shuffled" randomly. Any
reliable difference (assuming that the locations of the peaks are reliably
obtained from different observations of the same system) between them
suggests that there is some temporal structure that is not explicable in
terms of the distributions of inter-event intervals. You might want to do a
search on "information carried forward." Don't know if that will be
fruitful. Of course, there are traditional Fourier transforms and power
spectra.
"mat" <mats_trash at hotmail.com> wrote in message
news:43525ce3.0401220431.67b6948f at posting.google.com...
> Can anyone direct me to literature on useful metrics of regularity in
> spike series. I've plotted autocorrelograms to see if any whopping
> peaks appear but to no avail. On cursory reading I came accros papers
> on 'Approximate Entropy' but I don't think my datasets contain enough
> spikes to make this a valid measure.
>> Many thanks