Figure 6. | Demixed principal component analysis of neural population data

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Demixed principal component analysis of neural population data

Figure 6.

Affiliation details

Champalimaud Centre for the Unknown, Portugal; École Normale Supérieure, France; Centre for Integrative Neuroscience, University of Tübingen, Germany; Wake Forest University School of Medicine, United States; Cold Spring Harbor Laboratory, United States; Universidad Nacional Autónoma de México, Mexico; El Colegio Nacional, Mexico; Harvard University, United States
Figure 6.
Download figureOpen in new tabFigure 6. Demixed PCA applied to recordings from rat OFC during an olfactory categorization task (Kepecs et al., 2008).

Same format as Figure 3 (a) Cartoon of the paradigm, adapted from Wang et al. (2013). (b) Each subplot shows one demixed principal component. In each subplot there are ten lines corresponding to ten conditions (see legend). Six out of these ten conditions were rewarded and are shown with thick lines; note that the pure left (red) and the pure right (blue) odors did not have error trials. Inset shows mean rate of the second interaction component during the anticipation period. (c) Cumulative variance explained by PCA and dPCA components. (d) Explained variance of the individual demixed principal components. Pie chart shows how the total signal variance is split between parameters. (e) Upper-right triangle shows dot products between all pairs of the first 15 demixed principal axes, bottom-left triangle shows correlations between all pairs of the first 15 demixed principal components.

DOI: http://dx.doi.org/10.7554/eLife.10989.009