Using previously described methodologies, we computed a phase locking index (PLI) for each neuron using the relation <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0009521.e037.PNG" class= inline-graphic"/>, where max(STRF) and min(STRF) are the maximum and minimum values in the STRF, and r is the average firing rate [40].
The distribution of phase locking index was examined in each group.
In this article, we investigate a measure called the phase locking index (PLI), which is associated with the nonlinear bSPM.
The phase locking index (PLI) was introduced to quantify in a statistical sense the phase synchronization of two signals.
Namely, gene pairs with the value of their phase locking index at least two standard deviations above mean of all pairs are considered significantly phase locked.
The influence of oscillatory amplitude and phase can be estimated by calculating power and phase locking (e.g., by the phase locking index, PLI cf. Schack and Klimesch, 2002).
Based on the results, we will propose a new network inference approach utilizing the phase locking index, and examine the modular structure of the networks constructed and the biological themes shared by genes in the network modules.
We first examined the distribution of phase locking index λ in all four datasets of he Stanford yeast cell cycle study [ 33, 34], the histograms are given in Figure 1.
In this study, we considered locking with Max{ m, n} up to 4, and define the final phase locking index to be (8) λ = max { λ m, n, m, n = 1, 2, 3, 4 } Here max is used because the two genes are considered phase locked if the value of anyone of the λ m, n 's, is high.
You can check for this by locking the index arm at 60 degrees and looking through the index mirror.
For example, Kolev and colleagues (2001) investigated phase locking during passive listening to repeated stimuli and active counting of target stimuli and found condition-specific phase-locking indices of alpha oscillations.
