Error bar of partitioning results of ten datasets in the WNFNs, and LNCGs conditions using the NJW algorithm.

<p>Error bar represents the standard error.</p

Predictions of trial outcomes in the LNCGs.

<p>(A) Results of Y-maze datasets. (B) Results of U-maze datasets.</p

Unsupervised clustering partitioning accuracy of Y-maze and U-maze datasets in the LNCGs using NJW and Ncut. (A) Results of Y-maze datasets.

<p>(B) Results of U-maze datasets.</p

Method overview of construction of WNFNs of two trials of a Y-maze task dataset.

<p>(A) A rat performed the L-choice trial. (B) Raster plot of thirteen neurons recorded in this trial. (C) Pearson correlation matrix between pairs of neurons. (D) Neuronal functional network of these neurons. (E) – (H) Illustration of the procedure for construction of neuronal functional networks for the R-choice trial.</p

Description of procedure for dividing two networks into the best community structures.

<p>(A) WNFN in the L-choice task (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074298#pone-0074298-g002" target="_blank">Figure 2D</a>). (B) WNFN in the R-choice task (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074298#pone-0074298-g002" target="_blank">Figure 2H</a>). (C) Different Q values corresponding to the different number of communities in the L-choice task. (D) Different Q values corresponding to the different number of communities in the R-choice task. (E) LNCGs based on the maximum Q value in the L-choice task. (F) LNCGs based on the maximum Q value in the R-choice task.</p

Unsupervised clustering partitioning accuracy of Y-maze and U-maze datasets in the WNFNs using NJW and Ncut. (A) Results of Y-maze datasets.

<p>(B) Results of U-maze datasets.</p

Two different trial types in the U-maze task. The rat ran back and forth along a rectangular track and received the water rewards at both ends of the track (the green and red points).

<p>(A) A rat running from the start point to the end point of the track in a clockwise direction, indicating the completion of a trial. (B) A rat finishing a different trial, having moved in a counterclockwise direction.</p

Predictions of trial outcome were based on WNFNs and LNCGs using the KNN prediction method.

<p>Error bar represents the standard error.</p

Unsupervised clustering trials and predictions of trial outcomes based on WNFNs and LNCGs.

<p>(A) Ncut results. (B) KNN results.</p

Implementation of the predictive classifier proposed in this paper.

<p>A number of trials containing R-choices and L-choices will be trained into a two-cluster classifier. If there is a new trial which we do not know its trial type in advance, connection patterns of this trial will be measured in the pattern layer to decide its trial type.</p