Multichannel Systems NeuroExplorer - Manual (Page 94)

The Results sheet shows for each neuron the weights this neuron has in all the eigenvectors.
Algorithm
Step 1.
Rate histograms are calculated for each of the selected neurons.
The time axis is divided into bins. The first bin is [0, Bin). The second bin is [Bin, Bin*2), etc. The left
end is included in each bin, the right end is excluded from the bin. For each bin, the number of events
(spikes) in this bin is calculated.
For example, for the first bin
bin_count = number of timestamps (ts) such that ts >= 0 and ts < Bin
Bin counts are calculated in such a way for all the selected variables resulting in a matrix
bin_count[i, j],
where i is the neuron number, j is the bin number.
Step 2.
The matrix of correlations between neurons c[t, s] is calculated:
c[t, s] = correlation between vectors bin_count[t, *] and bin_count[s, *], s, t = 1, ..., number of selected
neurons.
Step 3
The eigenvalues and eigenvectors are calculated for the matrix c[t, s]. The eigenvectors (principal
components) are sorted according to their eigenvalues. The first principal component has the largest
eigenvalue.
Each principal component becomes a new population vector in the data file.
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