Triple Configuration of Brain Networks Based on Recurrent Neural Networks: The Synergistic Effects of Exogenous Stimuli, Task Demands, and Spontaneous Activity

In a paper submitted to arXiv (arXiv:2604.23525), researchers Binghao Yang and Guangzong Chen propose a computational framework using Recurrent Neural Networks (RNNs) with neural dynamic constraints to model source-localized resting-state EEG data from 114 participants. They aim to decode how brain networks are dynamically configured by three factors: exogenous stimuli (external inputs), task demands (information processing goals), and spontaneous activity (internal neural noise). The model identifies the parietal network as a critical hub that supports these multiple configuration patterns, revealing that anterior and posterior parietal regions exhibit distinct functional specializations under different stimulus modalities.

This triple configuration framework formalizes a way to separate latent factors of brain dynamics, underscoring the computational significance of parietal regions in orchestrating higher-order intelligence. The work bridges computational neuroscience and AI by using RNNs to model neural dynamics, offering a data-driven method to understand cognitive flexibility. It suggests that future AI architectures could benefit from mimicking this hierarchical, multi-factor configuration, potentially leading to more adaptive and context-aware models. The study is currently under review and available as a preprint on arXiv.