Learning Time-Varying Multi-Region Brain Communications via Scalable Markovian Gaussian Processes

Weihan Li, Yule Wang, Chengrui Li, Anqi Wu

International Conference on Machine Learning 2025 · Oral

In this insightful talk from ICML 2025, Weihan Li and collaborators from Georgia Tech presented a novel approach to decipher the complex, dynamic communication patterns within the brain. The work, titled "Learning Time-Varying Multi-Region Brain Communications via Scalable Markovian Gaussian Processes," addresses a fundamental challenge in neuroscience: understanding how different brain regions exchange information, not just in a static sense, but as these interactions evolve over time during cognitive processes. The key innovation lies in a scalable framework that can model both the direction and speed of information flow between multiple brain areas, accounting for their inherently dynamic nature.

AI review

A technically competent paper that connects multi-output non-separable Gaussian processes to state-space models for the purpose of inferring time-varying directional brain communication. The GP-SSM link for non-separable multi-output kernels is the cleanest contribution and has some genuine utility. The neuroscience application is well-motivated and the experimental results are biologically plausible. However, the 'universality' claim deserves scrutiny — converting stationary GPs to SSMs via spectral methods or controllable canonical forms is well-trodden territory, and the novelty rests on…