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

The study investigates whether a biological wiring diagram can serve as a computational substrate for tasks beyond its evolutionary purpose. Using the MaleCNS v1.0 Drosophila connectome as a fixed recurrent network, the authors train models to perform bounded addition and a grounded relational language task, achieving high accuracy (92.77% for addition and 61.59% for language) that surpasses degree-preserving rewired controls. The results demonstrate that the higher-order structure of the MaleCNS provides a reusable inductive bias for these cognitive tasks.

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