Drosophila brain wiring supports learning human cognitive tasks
A Drosophila Whole-Connectome Network Can Learn Human-Designed Cognitive Tasks
Machine Learning
Summary
This paper shows that the detailed wiring of a fruit fly’s brain can be used as a starting point for machines to learn tasks designed by humans, like simple addition and understanding language relationships. The authors used a full brain map from one male fruit fly to build an artificial network and trained it on specific cognitive tests. Their models performed better than random rewired networks, suggesting the fly’s brain wiring contains helpful patterns for learning. This means biological brain structures might offer useful guidance for building smarter artificial systems.
What this means in practice
- •For artificial intelligence developers: Use insect brain wiring patterns as a fixed network structure to improve the learning of arithmetic and language tasks in AI models.
- •For robotics control engineers: Implement fixed biological network topologies to create controllers capable of relational reasoning and arithmetic calculation without redesigning network connections.
Authors
Joonghui Cho, Minchan Kang, Daeshik Kim
Abstract
Can a biological wiring diagram serve as a useful computational substrate beyond the behaviors for which it evolved? We use the publicly released MaleCNS v1.0 connectome, reconstructed from a single adult male Drosophila specimen, as the fixed recurrent topology of an artificial network. We train separate models for bounded addition and for a controlled grounded relational language task built from a fixed 100-word lexicon. In both models, one scalar is learned per anatomical edge. The anatomical graph reaches 92.77% mean accuracy on held-out addition, compared with 67.93% for directed degree-preserving rewires. On the strict paired language endpoint, which matches original and order-reversed scenes to their corresponding descriptions, it reaches 61.59% across four fixed interfaces, compared with 44.17% for matched rewires. At the canonical interface, it ranks first in a fixed 21-graph comparison. On the matched 48-group intervention subset, shuffling task-defined sensory features reduces its score from 60.94% to 19.27%. Together, these results show that higher-order MaleCNS wiring provides a reusable inductive bias for bounded addition and grounded relational language.