Fig. 5 - Supplemental 2

Classification of odor representations (template matching). (A) Pattern classification probability quantified by template matching (see Methods). PTarget quantifies the probability that an activity pattern from the test set is assigned to a target class from the training set (see Figure 5A) and is plotted as a function of the similarity (Pearson correlation) between the test and target odor patterns in the olfactory bulb (OB). Unlike quadratic discriminant analysis (QDA), template matching can also be applied to outputs of Scaled networks. Note enhanced classification probability for patterns evoked by odors similar to learned odors in Tuned E+I networks, and to a lesser extent in Scaled networks. (B) The dataset was divided into two categories, depending on the trajectory in the odor subspace (insets; n = 484 activity patterns each), and template matching was performed again on each of the generated datasets. Note that learned odors were presented further apart in time in the top panels than in the bottom panels. Left: PTarget as a function of similarity in the OB, see panel A. Right: Classification probability for patterns similar to the training set (see A). Note that odor classification by Scaled networks depends on the trajectories in the odor subspace. (C) Projection of activity patterns in response to the odor subspace onto the first two principal components (PCs) in Scaled networks. Odor representations depend on stimulus history: note the reduced separation between representations of different odor classes during trajectories morphing one learned odor into another learned odor (arrows). This effect arises from the attractor dynamics of the networks.