We found that models trained with as few as five top-ranked sessions outperformed those with randomly chosen sessions even when the full dataset was used, demonstrating the impact of session-to-session variability in performance scaling. (6/9)

For the forward-prediction task that did exhibit consistent scaling, we identified implicit data heterogeneity arising from cross-session variability. We proposed a session-selection procedure based on single-session finetuning performances. (5/9)

In this work, we systematically investigate how data heterogeneity impacts the scaling behavior of neural data transformer. We first found that brain region mismatches among sessions reduced scaling benefits of neuron-level and region-level activity prediction performances. (4/9)