  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.
