Fig. 4: Pairwise interactions between pairs of nonlabeled neurons and pairs of neurons projecting to the same target.

a, Schematic of the models to compute pairwise joint probability density functions and conditional joint probability density functions of two neurons with correlated activities (\({r}_{1},{r}_{2}\)) as a function of time (t). A vector of movement variables (x) with components (\({x}_{1},\ldots ,{x}_{n})\) is represented. Using single-neuron NPvC model outputs, we build different types of pairwise correlation models with or without conditioning over the movement variables. The joint pairwise model is then used to estimate noise correlations or interaction information. b, Left: noise correlations computed for pairs of nonlabeled neurons and pairs of neurons projecting to the same area for correct and incorrect trials. Right: same except for noise correlations conditioned on movement variables. c, Similar to b but for interaction information. d, Average single-neuron choice information in different populations during the first 2 s after the test onset for correct and incorrect trials over all nonlabeled and projection cells. e, Histogram of interaction information divided into pairs of IE (red), IL (blue) and independent pairs (green). In b–d the average is computed over all the simultaneously recorded pairs of nonlabeled or same-target projection cells. Error bars indicate mean ± s.e.m. across all pairs of neurons. *P < 0.05, and ***P < 0.001, t test with two-sided Holm–Bonferroni correction for statistical multiple comparisons. Nonlabeled, n = 145,439 pairs; same projection, n = 1,355 pairs.