FLYNN: Robust Neural Network for Robot Navigation using Fly Brain Topology
Researchers developed FLYNN, a recurrent neural network based on the complete brain connectome of the fruit fly, for vision-based navigation. Compared to traditional networks, FLYNN exhibits superior robustness to out-of-distribution data and sensory loss, remaining functional even under total vision loss.
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[Submitted on 21 Jun 2026]
Title:FLYNN: Robust Neural Network for Robot Navigation using Fly Brain Topology
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Abstract:While deep learning models achieve state-of-the-art performance in complex tasks, they remain brittle when faced with new environments or sensory deprivation. In contrast, biological systems exhibit remarkable tolerance to these challenges. We address this vulnerability by developing a recurrent neural network (RNN) whose architecture is directly derived from the synaptic-resolution brain connectome of the fruit fly Drosophila melanogaster. We demonstrate the feasibility of training the fly connectome neural network (FLYNN) to perform vision-based navigation in MuJoCo, achieving performance comparable to modern hand-crafted networks of similar parameter counts. Crucially, FLYNN exhibits superior resistance to out-of-distribution (OOD) data and tolerance to sensory loss without further training. It remained functional even under total vision loss while hand-crafted networks largely failed, even when specifically trained with camera dropout. Principal Component Analysis (PCA) of the internal state of FLYNN suggests that it exhibits a particularly high degree of representational modularity, which might be related to its robustness. Our work provides a new direction for designing resilient artificial agents following the topology of biological brains.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.00025 [cs.RO]
(or arXiv:2607.00025v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.00025
arXiv-issued DOI via DataCite
Submission history
From: Benquan Wang [view email] [v1] Sun, 21 Jun 2026 02:41:31 UTC (5,145 KB)
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