Fig. 6.

The linear ramping component of each neuron’s firing rate drives the decoder’s ability to estimate the passage of time (compare with Fig. 5). (A) For each neuron, we calculate the linear fit to the average firing rate across trials during the same time interval as in Fig. 5. We then subtract this linear fit from the firing rate on every single trial and calculate the timing uncertainty. (B) After the linear ramping component is removed, the timing uncertainty in both the neural data (black curves) and trained RNN models (blue curves) is near chance level (red curves). In contrast, for the untrained RNN with stabilized chaotic dynamics (17), it is still possible to decode time with high accuracy, down to the limiting resolution set by the 100-ms time bin of the analysis, even after the linear component has been removed. Error bars show two SDs.