标题： 生物处理单元：利用昆虫连通组开创生物逼真神经架构 显示英文标题

标题： Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures

摘要： 果蝇幼虫大脑的完整连接组为研究生物进化电路是否能够支持人工智能提供了独特的机会。 我们将这种布线图转换为生物处理单元（BPU），这是一个直接从突触连通性派生的固定循环网络。 尽管其规模较小（3000个神经元和它们之间的65000个权重），未经修改的BPU在MNIST上达到98%的准确率，在CIFAR-10上达到58%，超过了尺寸匹配的多层感知器（MLP）。 通过结构化的连接组扩展来扩展BPU可以进一步提高CIFAR-10的性能，而模态特定的消融实验揭示了不同感觉子系统的不均衡贡献。 在ChessBench数据集上，仅在10000场比赛上训练的轻量级GNN-BPU模型实现了60%的走法准确率，比任何尺寸的Transformer高出近10倍。 此外，具有约2M参数的CNN-BPU模型优于参数匹配的Transformer，并且在推理时采用深度为6的极小极大搜索，达到了91.7%的准确率，超过了甚至一个9M参数的Transformer基线。 这些结果展示了生物保真神经架构支持复杂认知任务的潜力，并激励在未来的工作中扩展到更大更智能的连接组。 显示英文摘要

摘要： The complete connectome of the Drosophila larva brain offers a unique opportunity to investigate whether biologically evolved circuits can support artificial intelligence. We convert this wiring diagram into a Biological Processing Unit (BPU), a fixed recurrent network derived directly from synaptic connectivity. Despite its modest size 3,000 neurons and 65,000 weights between them), the unmodified BPU achieves 98% accuracy on MNIST and 58% on CIFAR-10, surpassing size-matched MLPs. Scaling the BPU via structured connectome expansions further improves CIFAR-10 performance, while modality-specific ablations reveal the uneven contributions of different sensory subsystems. On the ChessBench dataset, a lightweight GNN-BPU model trained on only 10,000 games achieves 60% move accuracy, nearly 10x better than any size transformer. Moreover, CNN-BPU models with ~2M parameters outperform parameter-matched Transformers, and with a depth-6 minimax search at inference, reach 91.7% accuracy, exceeding even a 9M-parameter Transformer baseline. These results demonstrate the potential of biofidelic neural architectures to support complex cognitive tasks and motivate scaling to larger and more intelligent connectomes in future work.