Trial Bin Counts

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. When used with continuous data, for each bin and reference vent, the average of the continuous values within the bin is calculated.

Parameters

Parameter	Description
Reference	Specifies a reference neuron or event.
XMin	Histogram time axis minimum in seconds.
XMax	Histogram time axis maximum in seconds
Bin	Bin size in seconds.
Normalization	Histogram units (Counts/Bin, Probability or Spikes/Second). See Algorithm below.
No Selfcount	An option not to count reference events if the target event is the same as the reference event (prevents a histogram to have a huge peak at zero when calculating PSTH versus itself).
Select Data	If Select Data is From Time Range, only the data from the specified (by Select Data From and Select Data To parameters) time range will be used in analysis. See also Data Selection Options.
Select Data From	Start of the time range in seconds.
Select Data To	End of the time range in seconds.
Interval filter	Specifies the interval filter(s) that will be used to preselect data before analysis. See also Data Selection Options.
Send to Matlab	An option to send the matrix of numerical results to Matlab. See also Matlab Options.
Matrix Name	Specifies the name of the results matrix in Matlab workspace.
Matlab command	Specifies a Matlab command that is executed after the numerical results are sent to Matlab.
Send to Excel	An option to send numerical results or summary of numerical results to Excel. See also Excel Options.
Sheet Name	The name of the worksheet in Excel where to copy the numerical results.
TopLeft	Specifies the Excel cell where the results are copied. Should be in the form CR where C is Excel column name, R is the row number. For example, A1 is the top-left cell in the worksheet.

Summary of Numerical Results

The following information is available in the Summary of Numerical Results

Column	Description
Variable	Variable name.
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. When used with continuous data, for each bin and reference vent, the average of the continuous values within the bin is calculated.

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 Spikes/Sec, bin counts are divided by Bin.