标题： SPINT：用于一致皮层内运动解码的空间排列不变神经变换器 显示英文标题

标题： SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding

摘要： 皮层内脑机接口（iBCI）旨在从神经元群体活动中解码行为，使运动障碍患者恢复运动功能和交流能力。 长期iBCI中的一个关键挑战是神经记录的非平稳性，其中记录群体的组成和调谐特性在不同记录会话中不稳定。 现有方法尝试通过显式对齐技术来解决这个问题；然而，它们依赖于固定的神经身份，并需要测试时的标签或参数更新，这限制了它们在会话间的泛化能力，并在部署时增加了额外的计算负担。 在本工作中，我们引入了SPINT——一种空间排列不变的神经变换器框架，用于直接在无序的神经单元集合上进行行为解码。 我们方法的核心是一种新颖的上下文相关的的位置嵌入方案，该方案动态地推断单元特定的身份，从而实现在不同记录会话间的灵活泛化。 SPINT支持可变大小群体的推理，并允许使用少量未标记的测试会话数据进行无梯度的少样本适应。 为了进一步提高模型对群体变异性的鲁棒性，我们引入了动态通道丢弃，这是一种针对iBCI的正则化方法，在训练期间模拟群体组成的变动。 我们在FALCON基准的三个多会话数据集上评估了SPINT，涵盖了人类和非人类灵长类动物的连续运动解码任务。 SPINT表现出稳健的跨会话泛化能力，在不依赖测试时对齐和微调的情况下，优于现有的零样本和少样本无监督基线。 我们的工作为长期iBCI应用的稳健且可扩展的神经解码框架迈出了一步。 显示英文摘要

摘要： Intracortical Brain-Computer Interfaces (iBCI) aim to decode behavior from neural population activity, enabling individuals with motor impairments to regain motor functions and communication abilities. A key challenge in long-term iBCI is the nonstationarity of neural recordings, where the composition and tuning profiles of the recorded populations are unstable across recording sessions. Existing methods attempt to address this issue by explicit alignment techniques; however, they rely on fixed neural identities and require test-time labels or parameter updates, limiting their generalization across sessions and imposing additional computational burden during deployment. In this work, we introduce SPINT - a Spatial Permutation-Invariant Neural Transformer framework for behavioral decoding that operates directly on unordered sets of neural units. Central to our approach is a novel context-dependent positional embedding scheme that dynamically infers unit-specific identities, enabling flexible generalization across recording sessions. SPINT supports inference on variable-size populations and allows few-shot, gradient-free adaptation using a small amount of unlabeled data from the test session. To further promote model robustness to population variability, we introduce dynamic channel dropout, a regularization method for iBCI that simulates shifts in population composition during training. We evaluate SPINT on three multi-session datasets from the FALCON Benchmark, covering continuous motor decoding tasks in human and non-human primates. SPINT demonstrates robust cross-session generalization, outperforming existing zero-shot and few-shot unsupervised baselines while eliminating the need for test-time alignment and fine-tuning. Our work contributes an initial step toward a robust and scalable neural decoding framework for long-term iBCI applications.