aiqbench analysis
GRAFT introduces a Transformer architecture that decouples reusable temporal dynamics from recalibratable neuron interfaces, enabling efficient adaptation to changing neural populations in long-term brain-computer interfaces. By updating only 9.21% of parameters to recalibrate across days, the model achieves state-of-the-art performance on the NLB'21 benchmark while addressing a key limitation in fixed read-in and readout layers. This approach offers a practical pathway for maintaining model utility in dynamic recording environments without requiring full retraining.
GRAFT: Gain-Recalibrated Adapters for Transformer-Based Neural Population Activity Modeling
arXiv:2606.11066v1 Announce Type: new Abstract: Neural population activity models can recover rich temporal structure from binned spikes, but their read-in and readout layers often remain tied to a fixed set of recorded neurons. This coupling limits reuse in long-term brain-computer interfaces, where recorded neuron identities, counts, and response statistics can change across days. We introduce GRAFT, a Transformer-based neural population activity model that separates reusable temporal dynamics from a recalibratable neuron interface. The neuron interface controls how recorded neurons enter and leave the shared backbone, and auxiliary gain and positional mechanisms support neural activity modeling inside the Transformer. On MC Maze under the standard NLB'21 protocol, GRAFT reaches 0.3866 co-bps as an ensemble, setting a new state of the art on the primary co-bps metric among public and reported NLB'21 results. In a cross-day protocol constructed from the NLB'21 MC Maze dataset series, GRAFT recalibrates from MC Maze to the scaled MC Maze datasets (Large/Medium/Small) by updating only 9.21% of parameters, reaching 0.3749, 0.3112, and 0.3152 co-bps with restricted target-day support sets. These results show that the same interface-backbone separation supports both strong Transformer-based neural population activity modeling and data-efficient cross-day recalibration.
Abstract:Neural population activity models can recover rich temporal structure from binned spikes, but their read-in and readout layers often remain tied to a fixed set of recorded neurons. This coupling limits reuse in long-term brain-computer interfaces, where recorded neuron identities, counts, and response statistics can change across days. We introduce GRAFT, a Transformer-based neural population activity model that separates reusable temporal dynamics from a recalibratable neuron interface. The neuron interface controls how recorded neurons enter and leave the shared backbone, and auxiliary gain and positional mechanisms support neural activity modeling inside the Transformer. On MC Maze under the standard NLB'21 protocol, GRAFT reaches 0.3866 co-bps as an ensemble, setting a new state of the art on the primary co-bps metric among public and reported NLB'21 results. In a cross-day protocol constructed from the NLB'21 MC Maze dataset series, GRAFT recalibrates from MC Maze to the scaled MC Maze datasets (Large/Medium/Small) by updating only 9.21% of parameters, reaching 0.3749, 0.3112, and 0.3152 co-bps with restricted target-day support sets. These results show that the same interface-backbone separation supports both strong Transformer-based neural population activity modeling and data-efficient cross-day recalibration.
Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
arXiv:2606.11066 [cs.LG]
(or arXiv:2606.11066v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.11066 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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