When Connectomic Wiring Hinders Imitation: Interface Constraints and the Latency of Sensory Access

Fly connectomes are increasingly proposed as architectural priors for neural controllers, with reports that a policy structured by the wiring diagram learns better than random graphs. We test this on four connectomes (male and female ventral nerve cords, two brains) with 14 null models, two input/output (IO) designs and three motor tasks. Under gradient-based imitation the real wiring never outperforms a random graph. With a dense trainable IO we find no consistent effect of topology; with a biologically wired IO (per-leg sensing and actuation, commands through descending neurons) the real wiring is worse, error rises monotonically with structural fidelity, and the effect replicates in the other sex and on a hexapod robot. Hubs, modularity, reciprocity and spectral properties do not explain the penalty; the scarcity of direct afferent→efferent edges does. Degree-preserving swaps that add such edges bring the connectome to the level of a degree-matched random graph, sham swaps do not, and only sensory→motor shortcuts help. The penalty is unchanged under a teacher with no causal dependence on other legs, halves when the teacher tolerates two to three ticks of sensory delay (the connectome's afferent-to-motor path length), and lies in the slow, gait component of the target. The wiring diagram withholds one-tick paths from sensory populations whose fast fluctuations predict the gait; an accessibility–demand model ranks 27 graph variants (ρ = 0.92). Under our implementation the published positive result does not survive an Erdős–Rényi baseline.