[
    {
        "id": "osp-16638",
        "type": "article-journal",
        "title": "A Drosophila Whole-Connectome Network Can Learn Human-Designed Cognitive Tasks",
        "author": [
            {
                "family": "Cho",
                "given": "Joonghui"
            },
            {
                "family": "Kang",
                "given": "Minchan"
            },
            {
                "family": "Kim",
                "given": "Daeshik"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/a-drosophila-whole-connectome-network-can-learn-human-designed-cognitive-tasks",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]