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FLYNN: Fly Brain Topology Inspires Robust Robot Navigation Neural Network

Benquan Wang, Jingdao Chen· July 2, 2026 View original

Summary

This paper introduces FLYNN, a recurrent neural network whose architecture is derived from the fruit fly brain connectome, demonstrating superior robustness to out-of-distribution data and sensory loss in robot navigation tasks compared to hand-crafted networks. Its modular representation contributes to this resilience.

While deep learning models excel in complex tasks, they often exhibit fragility when confronted with unfamiliar environments or sensory deprivation. Biological systems, in contrast, demonstrate remarkable resilience to such challenges. This research addresses the vulnerability of artificial neural networks by developing a recurrent neural network (RNN) named FLYNN. FLYNN's architecture is directly inspired by the synaptic-resolution brain connectome of the fruit fly, Drosophila melanogaster. The study successfully trained FLYNN to perform vision-based navigation in a MuJoCo environment, achieving performance comparable to modern, hand-crafted networks with similar parameter counts. Crucially, FLYNN exhibited superior resistance to out-of-distribution data and maintained functionality even under significant sensory loss, such as total vision loss, without requiring further training. This contrasts sharply with hand-crafted networks, which largely failed even when specifically trained with camera dropout. Principal Component Analysis of FLYNN's internal states suggests a high degree of representational modularity, which is hypothesized to be a key factor in its observed robustness. This work opens new avenues for designing resilient artificial agents by drawing inspiration from biological brain topologies.

Why it matters

For professionals in robotics, autonomous systems, and AI hardware, FLYNN presents a paradigm shift in designing more robust and adaptable neural networks, potentially leading to more reliable robots capable of operating in unpredictable real-world conditions.

How to implement this in your domain

  1. 1Research the principles of neuromorphic computing and biologically inspired neural network architectures.
  2. 2Evaluate the robustness of existing robot navigation systems against sensory loss and novel environments.
  3. 3Experiment with incorporating architectural elements inspired by biological connectomes into your neural network designs.
  4. 4Develop rigorous testing methodologies for out-of-distribution data and sensory deprivation scenarios for autonomous agents.
  5. 5Explore hardware implementations that can efficiently support complex, biologically inspired recurrent neural networks.

Who benefits

RoboticsAutonomous VehiclesAerospaceDefenseIndustrial Automation

Key takeaways

  • Deep learning models are often brittle in new environments or with sensory loss.
  • FLYNN, a neural network based on the fruit fly brain, offers superior robustness.
  • It performs vision-based navigation comparably to hand-crafted networks.
  • FLYNN maintains functionality under total vision loss, unlike conventional models, due to representational modularity.

Original post by Benquan Wang, Jingdao Chen

"arXiv:2607.00025v1 Announce Type: cross 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 c…"

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Originally posted by Benquan Wang, Jingdao Chen on X · view source

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