Figure 4: Across-time and across-neuron correlations in PPC activity influence mouse’s choices.

Panels a-d refer to PPC data during the sound localization task. a, Task performance (fraction correct) is higher when neural population vectors encode the stimulus consistently across time. b, Task performance (fraction correct) is higher in trials with correct stimulus decoding, suggesting that stimulus information is used to inform behavior. Left. Task performance in trials with correctly decoded stimulus is higher when information is encoded consistently. Right. The opposite happens when information is decoded incorrectly. Thus, stimulus information in neural activity has a larger impact on choices when it is encoded consistently. c, Performance (fraction of deviance explained) in explaining single-trial choices of various readout models. Full model uses all predictors (neural and non-neural). The other models neglect information from selected predictors as follows. No Cons: neglects neural consistency; No Neural: neglects stimulus decoded from neural activity and neural consistency. Models indicated by abbreviations lin, quad, rbf use linear, quadratic or radial basis function Support Vector Machines (SVMs) respectively to decode stimulus from neural activity. If no such abbreviation is used, a linear SVM is intended d, Left: Best-fit coefficients of the Full readout model (β₀ and β_s are non-neural coefficients corresponding to bias and stimulus-related drive due to non-recorded neurons, ${\beta }_{\widehat{s}}$ is the coefficient of the predicted stimulus, and β_i1, β_i2 correspond to the consistency-dependent interaction terms between each predicted stimulus and the neural consistency, with positive values amplifying the predicted stimulus effect in consistent trials). Right: Readout efficacy estimated from the best-fit coefficients of the Full model, above the baseline-level due to non-neural predictors, for consistent and inconsistent population vectors, represented schematically as a readout map in the 2D response space similarly to Fig. 2. In panels a-c, for all comparisons, P=10⁻⁴, two-sided permutation test. In panels a-c, d left, errorbars report mean ± SEM across n=6 sessions and all time point pairs within a 1 s lag. In panel d-right, the gray levels represent mean over n=6 sessions and all time point pairs within a 1s lag.

Panels e-h refer to PPC data during the evidence accumulation task. e, Same as in a. f, Same as in b. g, Same as in c. h, Same as in d. In e-h, consistency and choices are computed from the activity of two neuronal pools. Also in the evidence accumulation task, stimulus information in neural activity has a larger impact on choices when it is encoded consistently, and choices depend critically on neural consistency across pools.

In panels e-g, for all comparisons, P=10⁻⁴, two-sided permutation test. In panels e-g, h left, errorbars report mean ± SEM across n=11 sessions, Early and Late Delay epochs, and n=100 random pool splits. In panel h-right, the gray levels represent mean over n=11 sessions, Early and Late Delay epochs, and n=100 random pool splits.