Multichannel Systems NeuroExplorer - Manual (Page 69)

Background Mean The mean of the histogram background for the peak and trough analysis.
See Peak and Through Statistics below.
Background Stdev The standard deviation of the histogram background for the peak and
trough analysis. See Peak and Trough Statistics below.
Peak Z-score Peak Z-score. See Peak and Trough Statistics below.
Peak/Mean Histogram peak value divided by the background mean value.
Peak Position Peak position (in seconds).
Peak Half Height The Y value of the half height of the peak, i.e.
histogram_background_mean + (peak-mean)/2.
Peak Width at Half
Height Peak width at the peak half height level (in seconds).
Trough Z-score Trough Z-score. See Peak and Trough Statistics below.
Trough/Mean Histogram trough value divided by the background mean value.
Trough Position Trough position (in seconds).
Trough Half Height The Y value of the half height of the trough, i.e.
histogram_background_mean + (trough-mean)/2.
Trough Width at Half
Height Trough width at the trough half height level (in seconds).
Algorithm
Crosscorrelogram shows the conditional probability of a spike at time t0+t on the condition that there
is a reference event at time t0.
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 ref[i] be the array of timestamps of the reference event,
ts[i] be the spike train (each ts is the timestamp).
For each timestamp ref[k]:
1) calculate the distances from this event (or spike) to all the spikes in the spike train:
d[i] = ts[i] - ref[k]
2) for each i:
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 reference events.
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 ref[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
Page 67