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

• URL: http://arxiv.org/abs/2507.10951v1
• Date: Tue, 15 Jul 2025 03:31:57 GMT
• Title: Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures
• Authors: Siyu Yu, Zihan Qin, Tingshan Liu, Beiya Xu, R. Jacob Vogelstein, Jason Brown, Joshua T. Vogelstein,
• Abstract summary: The complete connectome of the Drosophila larva brain offers a unique opportunity to investigate whether biologically evolved circuits can support artificial intelligence.<n>We convert this wiring diagram into a Biological Processing Unit (BPU), a fixed network derived directly from synaptic connectivity.<n>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 parameters.<n>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.
• Score: 5.459524614513537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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