Figure 14. | Demixed principal component analysis of neural population data

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Demixed principal component analysis of neural population data

Figure 14.

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 14.
Download figureOpen in new tabFigure 14. Some demixed components as given by three different demixing methods (rows) in various datasets and marginalizations (columns).

Empty subplots mean that the corresponding method did not find any components. All projections were z-scored to make them of the same scale. Barplots on the right show fractions of variance in each marginalization for each component (stimulus in blue, decision in red, interaction in purple, condition-independent in gray): 𝐝𝐗~ϕ2/𝐝𝐗~2. Barplots consisting of a single colour correspond to perfect demixing.