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

arXiv: https://arxiv.org/abs/2407.00397 · HTML: https://mlsanity.com/paper/2407.00397/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 29 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Gaussian Process Kernels for Pattern Discovery and Extrapolation](https://mlsanity.com/paper/1302.4245/) (2013)
- [Generalized Shape Metrics on Neural Representations](https://mlsanity.com/paper/2110.14739/) (2021)
- [Temporal Parallelization of Bayesian Smoothers](https://mlsanity.com/paper/1905.13002/) (2019)
- [Spectral Mixture Kernels for Multi-Output Gaussian Processes](https://mlsanity.com/paper/1709.01298/) (2017)
- [State-Free Inference of State-Space Models: The Transfer Function Approach](https://mlsanity.com/paper/2405.06147/) (2024)
- [Linear-time inference for Gaussian Processes on one dimension](https://mlsanity.com/paper/2003.05554/) (2020)
- [Hida-Mat'ern Kernel](https://mlsanity.com/paper/2107.07098/) (2021)
- [Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains](https://mlsanity.com/paper/2306.01802/) (2023)
- [A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation](https://mlsanity.com/paper/2209.02816/) (2022)
- [A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing](https://mlsanity.com/paper/2402.01263/) (2024)
- [State-space deep Gaussian processes with applications](https://mlsanity.com/paper/2111.12604/) (2021)

## Built on by (most cited first, 4 of 4)

- [Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models](https://mlsanity.com/paper/2410.09614/) (2024)
- [Fast Multi-Group Gaussian Process Factor Models](https://mlsanity.com/paper/2412.16773/) (2024)
- [Machine Learning Methods for Studying Latent Neural Activity Dynamics](https://mlsanity.com/paper/2606.10530/) (2026)
- [A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression](https://mlsanity.com/paper/2608.11917/) (2026)

## Abstract (arXiv)

Understanding and constructing brain communications that capture dynamic communications across multiple regions is fundamental to modern system neuroscience, yet current methods struggle to find time-varying region-level communications or scale to large neural datasets with long recording durations. We present a novel framework using Markovian Gaussian Processes to learn brain communications with time-varying temporal delays from multi-region neural recordings, named Adaptive Delay Model (ADM). Our method combines Gaussian Processes with State Space Models and employs parallel scan inference algorithms, enabling efficient scaling to large datasets while identifying concurrent communication patterns that evolve over time. This time-varying approach captures how brain region interactions shift dynamically during cognitive processes. Validated on synthetic and multi-region neural recordings datasets, our approach discovers both the directionality and temporal dynamics of neural communication. This work advances our understanding of distributed neural computation and provides a scalable tool for analyzing dynamic brain networks.