Understanding the ubiquitous phenomenon of neural synchronization across species and organizational levels is crucial for decoding brain function. Despite its prevalence, the specific functional role, origin, and dynamical implication of modular structures in correlation-based networks remains ambiguous. Using recurrent neural networks trained on systems neuroscience tasks, this study investigates these important characteristics of modularity in correlation networks. We demonstrate that modules are functionally coherent units that contribute to specialized information processing. We show that modules form spontaneously from asymmetries in the sign and weight of projections from the input layer to the recurrent layer. Moreover, we show that modules define connections with similar roles in governing system behavior and dynamics. Collectively, our findings clarify the function, formation, and operational significance of functional connectivity modules, offering insights into cortical function and laying the groundwork for further studies on brain function, development, and dynamics.

翻译：理解跨物种与组织层面的神经同步这一普遍现象对于解码大脑功能至关重要。尽管这一现象普遍存在，基于相关性的网络中模块化结构的具体功能角色、起源及其动力学含义仍不明确。本研究利用在系统神经科学任务上训练的循环神经网络，探究了相关性网络中模块化结构的这些重要特征。我们证明模块是功能连贯的单元，有助于专门化信息处理；并揭示模块由输入层到循环层的投射在符号与权重上的不对称性自发形成。此外，我们表明模块在控制系统行为与动力学的连接中扮演相似角色。综上，我们的发现阐明了功能连接模块的功能、形成及其运行意义，为理解皮层功能提供了见解，并为后续关于大脑功能、发育及动力学的研究奠定了基础。