Figure 1. | Demixed principal component analysis of neural population data

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Demixed principal component analysis of neural population data

Figure 1.

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 1.
Download figureOpen in new tabFigure 1. Existing approaches to population analysis, illustrated with recordings from monkey PFC during a somatosensory working memory task (Romo et al., 1999).

(a) Cartoon of the paradigm, adapted from Romo and Salinas (2003). Legend shows 12 experimental conditions. (b) Average per-condition firing rates (PSTHs) for four exemplary neurons out of N=832. Colors refer to stimulus frequencies F1 and line styles (dashed/solid) refer to decision, see legend in (a). (c) Fraction of cells, significantly (p<0.05, two-way ANOVA) tuned to stimulus and decision at each time point. (d) Left: Distribution of stimulus tuning effect sizes across neural population at F1 period (black arrow in c). Significantly tuned neurons are shown in dark gray. Right: Same for decision at F2 period (gray arrow in c). (e) The average of zero-centered PSTHs over all significantly tuned neurons (for neurons with negative effect size, the sign of PSTHs was flipped). Arrows mark time-points that were used to select the significant cells. (f) Fraction of cells, significantly (p<0.05, linear regression) tuned to stimulus and decision at each time point. (g) Distribution of regression coefficients of neural firing rates to stimulus (during F1 period) and decision (during F2 period). (h) Stimulus and decision components produced by the method of Mante et al. (2013). Briefly, neural PSTHs are weighted by the regression coefficients. (i) Fraction of variance captured by the first 20 principal components. (j) Distributions of weights used to produce the first six principal components (weights are elements of the eigenvectors of the N×N covariance matrix). (k) First six principal components (projections of the full data onto the eigenvector directions).