Figure 5. | Demixed principal component analysis of neural population data

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Demixed principal component analysis of neural population data

Figure 5.

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 5.
Download figureOpen in new tabFigure 5. Demixed PCA applied to recordings from rat OFC during an olfactory discrimination task (Feierstein et al., 2006).

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 four lines corresponding to four conditions (see legend). Two out of these four conditions were rewarded and are shown by thick lines. (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.10989.008