发表机构
Mississippi State University（密西西比州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于果蝇大脑连接组的循环神经网络FLYNN，用于视觉导航，在分布外数据和感官缺失下表现鲁棒，优于手工网络。
AI 中文摘要
虽然深度学习模型在复杂任务中达到了最先进的性能，但在面对新环境或感官剥夺时仍然脆弱。相比之下，生物系统对这些挑战表现出显著的耐受性。我们通过开发一个循环神经网络（RNN）来解决这一脆弱性，该网络的架构直接来源于果蝇 Drosophila melanogaster 的突触分辨率大脑连接组。我们证明了训练果蝇连接组神经网络（FLYNN）在MuJoCo中执行基于视觉的导航的可行性，其性能与参数数量相当的现代手工网络相当。关键的是，FLYNN在无需进一步训练的情况下，对分布外（OOD）数据表现出优越的抵抗力和对感官丧失的耐受性。即使在完全视觉丧失的情况下，它仍然保持功能，而手工网络即使经过专门训练以应对相机丢失，也大多失败。FLYNN内部状态的主成分分析（PCA）表明，它表现出特别高的表征模块化程度，这可能与其鲁棒性有关。我们的工作为遵循生物大脑拓扑结构设计弹性智能体提供了新方向。
英文摘要
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.
