Extended Data Fig. 5. Offline parameter sweeps show the effect of RNN parameters and architecture choices.

Black arrows denote the parameters used for real-time evaluation. Blue open circles show the performance of single RNN seeds, while thin blue bars denote the mean across all seeds. (a) Rolling z-scoring improves performance substantially relative to no feature adaptation (when testing on held-out blocks that are separated in time from the training data). (b) Training RNNs with day-specific input layers improves performance relative to using a shared layer across all days. (c) RNN performance using different neural features as input (SP=spike band power, TX=threshold crossing). Combining spike band power with threshold crossings performs better than either alone. It appears that performance could have been improved slightly by using a −3.5 RMS threshold instead of −4.5. (d) RNN performance using audio-derived mel frequency cepstral coefficients (“audio MFCC”) or neural features from the area 44 arrays. While the MFCCs yield poor but above-chance performance, word error rates from IFG recordings appear to be at chance level (~100%). (e) RNN performance as a function of “kernel size” (i.e., the number of 20 ms bins stacked together as input and fed into the RNN at each time step). It appears that performance could have been improved by using larger kernel sizes. (f) RNN performance as a function of “stride” (a stride of N means the RNN steps forward only every N time bins). (g) RNN performance as a function of the number of stacked RNN layers. (h) RNN performance as a function of the number of RNN units per layer. (i) RNN performance as a function of the number of prior days included as training data. Performance improves by adding prior days, but with diminishing returns. The blue line shows the average word error rate across 10 RNN seeds and 5 evaluation days. Vertical lines show standard deviations across the 10 seeds.