Task-structured Modularity Emerges in Artificial Networks and Aligns with Brain Architecture

Understanding how neural systems develop modular organization is fundamental to both neuroscience and artificial intelligence. Although modular architectures can improve adaptability and cognitive performance, the processes leading to their emergence are poorly understood. Here we demonstrate that multitask and incremental learning enhance modularity in recurrent neural networks (RNNs) compared to single-task learning, revealing how functional demands influence the structural organization of neural networks. We trained RNNs on cognitive tasks under three distinct learning paradigms: single-task, simultaneous multitask, and incremental multitask learning. Our results suggest that networks adopting multitask learning show an enhanced degree of modularity compared to single-task training, especially when the task load exceeds the network's representation capacity. Those trained with incremental multitask learning, in particular, develop the highest degree of modularity, maintaining superior performance even when connections are selectively pruned. Furthermore, these task-induced networks exhibit structural properties more closely resembling those of biological brain networks than those based solely on spatial constraints, particularly in clustering coefficients and edge length distributions. Collectively, these findings suggest that modular brain architecture emerges not only from physical constraints but as an adaptive response to the sequential introduction of complex cognitive tasks, providing a causal explanation for how functional demands shape network topology. Our findings also illustrate how the developmental and evolutionary context of the brain, wherein multiple tasks are learned, can inform the design of artificial systems well-suited for similar environments.