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Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple
Weihan Li1, Chengrui Li1, Yule Wang1
1School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, USA.
Proceedings of Machine Learning Research
|November 1, 2024
Summary
We introduce a new model, the Multi-Region Markovian Gaussian Process (MRM-GP), combining Gaussian Processes and Linear Dynamical Systems. This approach enhances understanding of brain region communication by modeling frequencies and phase delays.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Statistical Modeling
Background:
- Understanding neural communication across brain regions is vital in neuroscience.
- Existing methods like Gaussian Processes (GP) and Linear Dynamical Systems (LDS) have limitations in capturing complex brain interactions.
- GPs excel at identifying latent variables and frequency bands, while LDS offer computational efficiency but limited expressiveness.
Purpose of the Study:
- To develop a novel statistical framework that integrates the strengths of both GP and LDS models.
- To create a model capable of explicitly representing frequencies and phase delays in neural data.
- To enable efficient and interpretable analysis of multi-region brain communication.
Main Methods:
- We propose the Multi-Region Markovian Gaussian Process (MRM-GP), a novel model structured as a Linear Dynamical System that mirrors a multi-output Gaussian Process.
- This approach establishes a direct link between LDS and multi-output GP formalisms.
- The MRM-GP is designed to operate with a linear inference cost over time points.
Main Results:
- The MRM-GP successfully models frequencies and phase delays within the latent space of neural recordings.
- The model provides an interpretable, low-dimensional representation of neural activity.
- We demonstrate the model's ability to reveal communication directions between brain regions and separate oscillatory communications into distinct frequency bands.
Conclusions:
- The MRM-GP offers a powerful and computationally efficient method for analyzing complex neural communication patterns.
- This integrated approach enhances the interpretability of latent representations in multi-region brain recordings.
- Our findings advance the statistical toolkit for investigating brain connectivity and dynamics across different frequency spectra.
