Learning Time-Varying Multi-Region Brain Communications via Scalable Markovian Gaussian Processes
Authors: Weihan Li, Yule Wang, Chengrui Li, Anqi Wu
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our model using neural recordings from multiple regions of the brain during visual processing tasks (Semedo et al., 2019; Siegle et al., 2021). Our results demonstrate the method s capability to uncover how information flow patterns dynamically change across multi-region networks, offering new insights into the temporal organization of largescale neural circuits and advancing our understanding of distributed neural computation. We evaluate our model on three datasets. |
| Researcher Affiliation | Academia | 1School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, USA. Correspondence to: Anqi Wu <EMAIL>. |
| Pseudocode | No | The paper describes the Kalman EM algorithm and its steps in Appendix B, but it does not present it in a pseudocode block or a clearly labeled algorithm format. It uses mathematical equations and explanatory text. |
| Open Source Code | Yes | Code is available at https://github.com/ BRAINML-GT/Adaptive-Delay-Model. |
| Open Datasets | Yes | Two Brain Regions (Semedo et al., 2019; Zandvakili & Kohn, 2019): Simultaneous spike train recordings from a monkey s primary visual area (V1) and secondary visual cortex (V2)... Five Brain Regions (Siegle et al., 2021): Simultaneous spike train recordings from a mouse s primary visual cortex (VISp)... |
| Dataset Splits | Yes | We evaluate our model and baseline models by randomly splitting the data into training, validation, and testing sets with a ratio of 0.8, 0.1, and 0.1, respectively. |
| Hardware Specification | No | The paper mentions 'GPU parallelization' and 'modern hardware' but does not specify any particular GPU models, CPU models, or other detailed hardware specifications used for experiments. |
| Software Dependencies | No | The paper mentions 'MATLAB' in the context of a baseline (m DLAG) but does not provide specific version numbers for its own implementation's software dependencies. |
| Experiment Setup | Yes | The number of across-region and within-region latent dynamics follows previous works (Gokcen et al., 2022; Li et al., 2024b), where ma = 2 and mw = 2. The order P = 4 is selected based on performance evaluation on the validation dataset. ... we set ma = 2, mw = 1, and P = 5. ... we then conduct a grid search with 5-fold cross-validation to refine the number of across-region and within-region latent dynamics and the model order P. |