PRISM: Population Relational Inference for Streaming Motor Decoding

Intracortical brain–computer interfaces must remain robust to session-to-session neural drift while supporting causal, low-latency streaming inference. We introduce PRISM, Population Relational Inference for Streaming Motor Decoding, which separates session interpretation from per-bin prediction. PRISM uses an unlabeled target-session support recording once to construct and cache a Session interface; each subsequent query bin is decoded causally through two Channel SSM blocks, a Population head, HoloSSM, and a motor readout. The decoder maintains fixed-size recurrent state and requires neither target behavior labels nor test-time gradient updates. On the official Few-shot Algorithms for Consistent Neural Decoding (FALCON) private held-out evaluation, PRISM ranks second among few-shot-unsupervised (FSU) entries on all three tasks in the 20 September 2026 leaderboard snapshot, with held-out $R^2$ values of 0.424, 0.574, and 0.272 at official dimensionless normalized latencies of 0.0273–0.0413. On 16 held-out dates of the public Indy reaching dataset, the full model achieves date-averaged $R^2=0.551$. After one-time support calibration, the 262,782-parameter decoder processes each 20-ms query bin in 0.484 ms on one CPU thread. PRISM therefore converts session-level adaptation into a reusable interface while retaining compact, fixed-state, low-latency causal inference.