Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions
CoRR(2024)
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 such as
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 is the first to establish 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.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要