Extended Data Fig. 4: Changes in neural recordings across days.

a, To visualize how much the neural recordings changed across time, decoded pen tip trajectories were plotted for two example letters (“m” and “z”) for all ten days of data (columns), using decoders trained on all other days (rows). Each session is labeled according to the number of days passed relative to Dec. 9, 2019 (day #4). Results show that although neural activity patterns clearly change over time, their essential structure is largely conserved (since decoders trained on past days transfer readily to future days). b, The correlation (Pearson’s r) between each session’s neural activity patterns was computed for each pair of sessions and plotted as a function of the number of days separating each pair. Blue circles show the correlation computed in the full neural space (all 192 electrodes) while red circles show the correlation in the “anchor” space (top 10 principal components of the earlier session). High values indicate a high similarity in how characters are neurally encoded across days. The fact that correlations are higher in the anchor space suggests that the structure of the neural patterns stays largely the same as it slowly rotates into a new space, causing shrinkage in the original space but little change in structure. c, A visualization of how each character’s neural representation changes over time, as viewed through the top two PCs of the original “anchor” space. Each “o” represents the neural activity pattern for a single character, and each “x” shows that same character on a later day (lines connect matching characters). The left panel shows a pair of sessions with only two days between them (“Day −2 to 0”), while the right panel shows a pair of sessions with 11 days between them (“Day −2 to 9”). The relative positioning of the neural patterns remains similar across days, but most conditions shrink noticeably towards the origin. This is consistent with the neural representations slowly rotating out of the original space into a new space, and suggests that scaling-up the input features may help a decoder to transfer more accurately to a future session (by counteracting this shrinkage effect). d, Similar to Fig. 3b, copy typing data from eight sessions was used to assess offline whether scaling-up the decoder inputs improves performance when evaluating the decoder on a future session (when no decoder retraining is employed). All session pairs (X, Y) were considered. Decoders were first initialized using all data from session X and earlier, then evaluated on session Y under different input scaling factors (e.g., an input scale of 1.5 means that input features were scaled up by 50%). Lines indicate the average raw character error rate and shaded regions show 95% CIs. Results show that when long periods of time pass between sessions, input-scaling improves performance. We therefore used an input scaling factor of 1.5 when assessing decoder performance in the “no retraining” conditions of Fig. 3.