发表机构
Seoul National University（首尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对神经数据跨会话泛化失败的问题，提出基于行为事件分割群体状态的语义标记化方法TWS，无需神经元或会话嵌入，在跨物种迁移中优于每神经元基础模型。
AI 中文摘要
细胞外电生理记录在每个会话中记录不同的神经元集合。神经基础模型将每个神经元和每个会话嵌入到其标记中，因此每个新会话都是它们从未见过的输入，它们无法泛化到新会话。新会话的标记器需要一个每个会话共享且携带行为的单元。群体活动提供了这样的单元。它在低维流形上演化，该流形在神经元更替中持续存在，并且在会话对齐后跨动物持续存在。该流形在任务事件（如刺激开始和运动开始）处改变状态。在每个状态内，群体占据一个状态，即它在两个事件之间跨越的流形部分，每个状态携带其自身的行为意义。我们提出带状态的标记化（TWS），它在这些事件处分割每个试验（任务的一次重复），并将每个状态转换为群体几何的标记，无需神经元或会话嵌入。在留出的国际脑实验室（IBL）会话上，TWS甚至从携带目标信息的边界解码运动，而一个在该会话上预训练的每神经元基础模型解码为随机水平。仅在老鼠上训练后冻结，TWS仅用线性探针迁移到猕猴和犹他阵列。在到达方向上，一个其边界未定义的目标，它实现了0.23的马修斯相关系数，而仅事件时间实现了$0.01$。对于跨会话泛化，标记比其上的模型更重要。
英文摘要
Extracellular electrophysiology records a different set of neurons in every session. Neural foundation models embed each neuron and each session into their tokens, so every new session is an input they have never seen, and they fail to generalize to it. A tokenizer for new sessions needs a unit that every session shares and that carries behavior. Population activity offers such a unit. It evolves on a low-dimensional manifold that persists across neuronal turnover and across animals once sessions are aligned. This manifold changes regime at task events such as stimulus onset and movement onset. Within each regime the population occupies a state, the part of the manifold it spans between two events, and each state carries its own behavioral meaning. We propose Tokenization with States (TWS), which segments each trial, one repetition of the task, at these events and converts every state into tokens of population geometry, with no neuron or session embedding. On held-out International Brain Laboratory (IBL) sessions, TWS decodes movement even from regime boundaries that carry no information about the target, while a per-neuron foundation model pretrained on those sessions decodes at chance. Frozen after training on mice alone, TWS transfers to macaques and Utah arrays with only a linear probe. On reach direction, a target that its boundaries do not define, it achieves a Matthews correlation of 0.23, where the event time alone achieves $0.01$. For cross-session generalization, the token matters more than the model on top.
Comments10 pages, 3 figures
