Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures
This work pioneers biofidelic neural architectures for AI, showing potential for complex cognitive tasks, though it is incremental in scaling from insect connectomes.
The authors tackled the problem of whether biologically evolved circuits can support artificial intelligence by converting the Drosophila larva brain connectome into a Biological Processing Unit (BPU), achieving 98% accuracy on MNIST, 58% on CIFAR-10, and 91.7% on ChessBench with a depth-6 minimax search.
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.