Superfly: Learning Locomotion with a Fly Connectome

We present Superfly, a sensorimotor network built from the adult Drosophila brain and ventral nerve cord connectome for embodied locomotion. The policy maps inputs to body controls through about 23,000 neurons and 2.2 million directed edges, fixing each connection's endpoints, synapse count, and excitatory or inhibitory sign. Training learns nonnegative scales that convert fixed synapse counts into synaptic currents. Mappings based on known sensorimotor pathways route body feedback, movement commands, and previous controls to corresponding neuron populations. Each neuron maintains voltage across body steps, and decoder readouts map its activity to controls for a MuJoCo fly model. Behavioral cloning learns about 37,000 parameters from demonstrations by a flybody walking expert. Compared with a parameter-matched graph neural network (GNN), the policy achieves lower validation action error (MSE 0.069 versus 0.096) and travels farther in five-second walking trials (8.5 versus 0.4 cm toward a 10 cm target). Although training provides only expert body controls and no neural activity targets, turn commands selectively modulate neuron types with established roles in navigation and steering. These responses persist after rewiring, showing that turn-command selectivity does not require the exact synaptic partners. The rewired policy fits actions better offline but travels less in closed loop. This executable model links learned locomotion to identified biological neurons and predicts their activity during behavior. We make our code, checkpoints, and an interactive website for exploring neuron activity available online at https://superfly-brain.vercel.app.