Figure 4. | Demixed principal component analysis of neural population data

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Demixed principal component analysis of neural population data

Figure 4.

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 4.
Download figureOpen in new tabFigure 4. Demixed PCA applied to recordings from monkey PFC during a visuospatial working memory task (Qi et al., 2011).

Same format as Figure 3. (a) Cartoon of the paradigm, adapted from Romo and Salinas (2003). (b) Demixed principal components. In each subplot there are ten lines corresponding to ten conditions (see legend). Color corresponds to the position of the last shown stimulus (first stimulus for t<2 s, second stimulus for t>2 s). In non-match conditions (dashed lines) the colour changes at t=2 s. Solid lines correspond to match conditions and do not change colors. (c) Cumulative variance explained by PCA and dPCA components. Dashed line marks fraction of signal variance. (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.10989.007