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

Nature Machine Intelligence, Published online: 28 September 2026; doi:10.1038/s42256-026-01306-9

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.

A neural network model of free recall learns multiple memory strategies

Nature Machine Intelligence, Published online: 20 July 2026; doi:10.1038/s42256-026-01274-0

Li et al. show that recurrent neural networks optimized for free recall discover diverse, human-like memory strategies beyond classical temporal context models, with top models using an index-based mechanism resembling the memory palace technique.
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