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arXiv Machine Learning By Sangyoon Bae, Jiook Cha

Neural Data Needs Semantic Tokenization: Behavioral Events as Boundaries of Session-Transferable Tokens

Read the original on arXiv Machine Learning →

The paper introduces Tokenization with States (TWS), a method that segments neural recordings into behaviorally meaningful states defined by task events and represents each state as a token based on population geometry, bypassing neuron- or session-specific embeddings. TWS successfully decodes movement from regime boundaries that carry no target information and transfers across species and recording modalities with only a linear probe. The approach demonstrates that tokens derived from shared population dynamics can generalize across sessions, outperforming per-neuron foundation models that fail to generalize.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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