A Drosophila Whole-Connectome Network Can Learn Human-Designed Cognitive Tasks

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.

14 pages, 4 figures. Code: this https URL

Machine Learning (cs.LG)

arXiv:2610.10014 [cs.LG]

(or arXiv:2610.10014v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.10014 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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