Supplementary Figure 3: Quantification of primary features in the surrogate datasets based on PFC data. | Nature Neuroscience

Supplementary Figure 3: Quantification of primary features in the surrogate datasets based on PFC data.

From: Structure in neural population recordings: an expected byproduct of simpler phenomena?

Supplementary Figure 3

Each surrogate dataset Xsurr(i) (for i {1, …,S}, S=100) has marginal covariances ΣT (i), ΣN (i), and ΣC (i), which, in the surrogate-TNC control (right column of each panel), should match the specified primary features ΣT, ΣN, and ΣC of the original neural data. The right column demonstrates that fit: the top two rows (panel a) of the right column shows that CFR and TME match the true covariances in expectation (see equation on vertical axis); the bottom two rows (panel b) show that CFR has very minor variance around that mean, whereas TME has meaningful variance (again see equation on vertical axis), as expected. Each dot corresponds to one of the two datasets collected in the prefrontal cortex (RR15 and RR14; see Methods for data details). The left and middle columns of each panel of the figure correspond to the surrogate-T (left) and surrogate-TN (middle) controls. Here we see as expected that all covariances that are specified in the control (ΣT in the left column; both ΣT and ΣN in the middle column) are very well matched. Correspondingly, we also see that covariances that are not specified in the control do not match the moments of the data. Taken together, these data demonstrate quantitatively that the CFR and TME methods behave as desired, both in terms of preserving the specified structure, and in terms of destroying structure not specified.

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