多区域马尔可夫高斯过程：一种发现跨多个大脑区域方向性通信的高效方法
Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions
AI总结：
本研究提出多区域马尔可夫高斯过程（MRM-GP），结合高斯过程与线性动力系统，以线性推断成本高效发现跨大脑区域的方向性通信并分离振荡频带。
摘要：
研究不同大脑区域之间的复杂交互在神经科学中至关重要。多种统计方法已经探索了跨多个大脑区域的潜在通信。两大主要类别是高斯过程（GP）和线性动力系统（LDS），各自具有独特优势。基于GP的方法能有效发现具有频带和通信方向的潜在变量。相反，基于LDS的方法计算效率高，但在潜在表示上缺乏强大的表达能力。在本研究中，我们通过创建一个镜像多输出GP的LDS，将两种方法合并，称为多区域马尔可夫高斯过程（MRM-GP）。我们的工作建立了LDS与多输出GP之间的联系，显式地对神经记录潜在空间中的频率和相位延迟进行建模。因此，该模型实现了随时间点的线性推断成本，并提供了可解释的低维表示，揭示了跨大脑区域的通信方向，并将振荡通信分离到不同频带中。
英文摘要：
Studying the complex interactions between different brain regions is crucial in neuroscience. Various statistical methods have explored the latent communication across multiple brain regions. Two main categories are the Gaussian Process (GP) and Linear Dynamical System (LDS), each with unique strengths. The GP-based approach effectively discovers latent variables with frequency bands and communication directions. Conversely, the LDS-based approach is computationally efficient but lacks powerful expressiveness in latent representation. In this study, we merge both methodologies by creating an LDS mirroring a multi-output GP, termed Multi-Region Markovian Gaussian Process (MRM-GP). Our work establishes a connection between an LDS and a multi-output GP that explicitly models frequencies and phase delays within the latent space of neural recordings. Consequently, the model achieves a linear inference cost over time points and provides an interpretable low-dimensional representation, revealing communication directions across brain regions and separating oscillatory communications into different frequency bands.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
