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Brain-computer interfaces decode neural activity, yet class-label supervision (CLS) leaves relationships among classes implicit. We propose Language as Supervision (LaS), which aligns neural features with natural-language class descriptions through learned projections and predicts classes using candidate text prototype...
NeuroPB is introduced, a framework that scales neural decoding by transferring knowledge from pretrained behavioral representations and achieves performance comparable to pretraining on macaque trajectories, demonstrating that transferable kinematic structure is shared across biological and artificial models.
FuzzyAlign, an alignment framework driven by fuzzy similarity, is proposed to establish a benchmark and explore the integration of large pretrained vision models with neural decoding, offering a high-performing and interpretable approach for bridging neural and artificial vision systems.
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