Association profile conditioning in a set-temporal transformer for cross-session intracortical motor decoding
Intracortical motor decoders degrade across sessions because the set of recorded units changes and persisting units can alter how their firing relates to behavior. Most existing methods update network weights on each new session or rely on unlabeled activity, which does not directly reveal such changes. We present APST, an Association Profile-conditioned Set-Temporal transformer that adapts to new sessions with all network weights frozen. From a few labeled calibration trials, APST summarizes how each unit's firing relates to behavior in a four-dimensional association profile computed in closed form. The profiles condition a set-attention encoder that accepts any number and order of units...
| Authors | Xinyuan Zhang, Handong Mo, Pengfei Wen et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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