Fig. 4.

Decoding time from neural activity reveals signatures of fixed-point dynamics. (A) Pixel (i,j) is the decode accuracy of a binary classifier trained to discriminate time points i and j using 100-ms bins of neural activity. The blocks of time where the decode is near chance level (50%) are signatures of fixed-point dynamics. The pattern of fixed points seen in the data agrees with the RNN model. In the model, the fixed points before stimulus onset store contextual information. Importantly, a linear classifier can easily discriminate other task-relevant quantities during these time intervals so the poor time decode is not simply due to noisy neural responses that have lost all informational content (SI Appendix, Fig. S10). (B) The average neural trajectories for all four trial types are plotted on the three principal components capturing most of the variance. Time is discretized in 100-ms nonoverlapping bins (denoted by dots) and shown from −500 to 300 ms relative to stimulus onset. During the fixed-point interval before stimulus onset, the trajectories cluster according to context.