Figure 3: RNN modeling of the CB task.

(a) Multi-area RNN configuration. The RNN received 4 inputs. The first two inputs indicated the identity of the left and right targets, which was red or green. These inputs were noiseless. The last two inputs indicated the value of the signed color coherence (proportional to amount of red in checkerboard) and negative signed color coherence (proportional to amount of green in checkerboard). We added independent Gaussian noise to these signals (see Methods). The network outputted two analog decision variables indicating evidence towards the right target (solid line) or left target (dashed line). A decision was made in the direction of whichever decision variable passed a preset threshold (0.6) first. The time at which the decision variable passed the threshold was defined to be the reaction time. (b,c) Psychometric and reaction time curves for exemplar multi-area RNN. (d) Area 1 and Area 3 principal components for exemplar RNN. (e) CCA correlation between each area and DLPFC principal components (left) and PMd principal components (right). DLPFC activity most strongly resembles Area 1, while PMd activity most strongly resembles Area 3. See also Fig. S3 where we computed CCA as a function of the number of dimensions. (f) Relative dPCA variance captured by the direction, color, and target configuration axes. Normalization makes direction variance equal to 1. Area 1 (3) variances more closely resemble DLPFC (PMd). (g) Area 1 has significantly higher decoding accuracies and (h) usable information compared to Area 3, consistent with DLPFC and PMd.