分离大脑多区域神经活动中的共享与特有动态，提升行为解码精度。
Coupled Transformer Autoencoder for Disentangling Multi-Region Neural Latent Dynamics
- 用耦合的Transformer架构建模非平稳、非线性神经动态。
- 显式划分潜空间为正交共享与私有子空间，分离跨区共性与区域特异性信号。
- 在运动与感觉皮层数据上表现更优，适合脑机接口与神经机制研究者。
从多个脑区同步记录数千个神经元的活动，揭示了跨区域共享与区域特异性动态的复杂混合。现有对齐或多视图方法忽略时间结构，而动态潜变量模型通常局限于单区域、假设线性读出或混淆共享与私有信号。我们提出耦合Transformer自编码器（CTAE）——一种统一框架，同时解决 (i) 非平稳、非线性动态和 (ii) 共享与区域特异性结构的分离。CTAE采用Transformer编码器与解码器捕捉长程神经动态，并显式将每个区域的潜空间划分为正交的共享与私有子空间。我们在两个高密度电生理数据集上验证了CTAE的有效性，分别来自运动皮层和感觉皮层区域。结果表明，相比现有方法，CTAE提取的表示能更准确解码行为变量。
原文摘要 · Abstract (English)
Simultaneous recordings from thousands of neurons across multiple brain areas reveal rich mixtures of activity that are shared between regions and dynamics that are unique to each region. Existing alignment or multi-view methods neglect temporal structure, whereas dynamical latent variable models capture temporal dependencies but are usually restricted to a single area, assume linear read-outs, or conflate shared and private signals. We introduce the Coupled Transformer Autoencoder (CTAE) - a sequence model that addresses both (i) non-stationary, non-linear dynamics and (ii) separation of shared versus region-specific structure in a single framework. CTAE employs transformer encoders and decoders to capture long-range neural dynamics and explicitly partitions each region's latent space into orthogonal shared and private subspaces. We demonstrate the effectiveness of CTAE on two high-density electrophysiology datasets with simultaneous recordings from multiple regions, one from motor cortical areas and the other from sensory areas. CTAE extracts meaningful representations that better decode behavioral variables compared to existing approaches.
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