Brain-inspired wiring economics for artificial neural networks.

ABSTRACT: Wiring patterns of brain networks embody a trade-off between information transmission, geometric constraints, and metabolic cost, all of which must be balanced to meet functional needs. Geometry and wiring economy are crucial in the development of brains, but their impact on artificial neural networks (ANNs) remains little understood. Here, we adopt a wiring cost-controlled training framework that simultaneously optimizes wiring efficiency and task performance during structural evolution of sparse ANNs whose nodes are located at arbitrary but fixed positions. We show that wiring cost control improves performance across a wide range of tasks, ANN architectures and training methods, and can promote task-specific structural modules. An optimal wiring cost range provides both enhanced predicti

SUBMITTER: Zhang XJ 

PROVIDER: S-EPMC11736432 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature