Task-structured modularity emerges in artificial networks and aligns with brain architecture

Abstract

Understanding how neural systems develop modular organization is fundamental to both neuroscience and artificial intelligence. Modular architectures are thought to be critical for adaptability and complex cognitive performance by enriching the brain’s ability to integrate and process information. While prior work posits that modularization emerges from physical constraints, such as minimizing metabolic wiring costs, these spatial models alone do not fully explain the functional organization of brain networks. A critical, unanswered question is how the functional demands of learning complex tasks computationally influence network topology. Here we demonstrate, using recurrent neural networks trained on cognitive tasks, how functional demands shape structural organization. We find that multitask learning paradigms enhance network modularity compared with single-task training, especially when task load strains the network’s capacity. Networks trained with incremental multitask learning, in particular, develop the highest degree of modularity and maintain superior performance. Furthermore, these task-induced networks exhibit structural properties that more closely resemble biological brain networks than models based solely on spatial constraints. These results provide a controlled computational account showing that functional demands can induce modular organization in artificial recurrent networks under capacity constraints, suggesting modularization as an adaptive response to the sequential introduction of complex tasks. Wu et al. show that neural networks learning multiple tasks organize into specialized modules, particularly under capacity constraints. Incremental multitask learning strengthens this modularity and produces architectures resembling brain networks.