Multichannel Systems NeuroExplorer - Manual (Page 86)

Color Scale Min Color scale minimum.
Color Scale Max Color scale maximum.
Reference Reverence event.
NumRefEvents Number of reference events.
Bin001Mean Mean of all the values for Bin 1.
Bin001SdDev Standard deviation of all the values for Bin 1.
Algorithm
Trial bin counts analysis computationally is essentially the same as the perievent histogram. The
difference is that the bin counts are saved for each reference event.
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]:
Set all bin counts to zero:
bincount[i] = 0 for all i
Calculate the distances from this event (or spike) to all the spikes in the spike train:
d[i] = ts[i] - ref[k]
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 Spikes/Sec, bin counts are divided by Bin.
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