Figure 3. Credit assignment to readout units.

(A) Schematic of offline classification model. (B-D) Classifier analyses results for the selected series (Fig. 1D). (B) Heatmap representation of the classifier weights to predict a single target (2) for the stable readout population on multiple days. (C) Success percentage (black; reproduced from Fig. 1D) and mean correlation coefficient (dark red) between classifier weights on consecutive days plotted across days. (D) Classification accuracy for all units (gray), readout units (red) and nonreadout units (blue) across training days. Error bars represent 95% confidence intervals on test accuracy (see Methods). (E) Early vs late classification accuracy for readout (shades of red) and nonreadout populations (shades of blue) across all series for both monkeys (readout-early vs readout-late : p = 0.0058 (**); readout-early vs nonreadout-early: p = 0.0019 (**); readout-late vs nonreadout-late: p = 0.0019 (**); nonreadout-early vs nonreadout-late: p = 0.13 (ns). For all comparisons in this panel, N = 10, Two-sided Wilcoxon Signed Rank test)