Multichannel Systems NeuroExplorer - Manual (Page 60)

Norm. Factor Normalization factor. Bin counts are divided by this value. See
Normalization in Algorithm below.
First Min. Time Position of the histogram minimum (in seconds). If there are multiple bins
in the histogram where the bin value equals to the histogram minimum, this
value represents the position of the first such bin.
First Max. Time Position of the histogram maximum (in seconds). If there are multiple bins
in the histogram where the bin value equals to the histogram maximum,
this value represents the position of the first such bin.
Algorithm
In general, the Autocorrelogram shows the conditional probability of a spike in the spike train at time t
on the condition that there is a spike at time zero.
The time axis is divided into bins. The first bin is [XMin, XMin+Bin). The second bin is [XMin+Bin,
Xmin+Bin*2), etc. The left end is included in each bin, the right end is excluded from the bin.
Let ts[i] be the spike train (each ts is the timestamp).
For each timestamp ts[k]:
calculate the distances from this spike to all other spikes in the spike train:
d[i] = ts[i] - ts[k]
for each i except i equal to k:
if d[i] is inside the first bin, increment the bin counter for the first bin:
if d[i] >= XMin and d[i] < XMin + Bin
then bincount[1] = bincount[1] +1
if d[i] is inside the second bin, increment the bin counter for the second bin:
if d[i] >= XMin+Bin and d[i] < XMin + Bin*2
then bincount[2] = bincount[2] +1
and so on... .
If Normalization is Counts/Bin, no further calculations are performed.
If Normalization is Probability, bin counts are divided by the number of spikes in the spike train.
Note that the Probability normalization makes sense only for small values of Bin. For Probability
normalization to be valid (so that the values of probability are between 0 and 1), there should be no
more than one spike in each bin. For example, if the Bin value is large and for each ts[k] above there
are many d[i] values such that d[i] >= XMin and d[i] < XMin + Bin, the bin count for the first bin can
exceed the number of spikes in the spike train. Then, the probability value
(bincount[1]/number_of_spikes) could be larger than 1.
If Normalization is Spikes/Sec, bin counts are divided by NumSpikes*Bin, where NumSpikes is the
number of spikes in the spike train.
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