Does the Wiring Matter? Testing a Connectome-Extracted Escape Circuit Against Its Own Randomized Control

Connectomics datasets now provide synapse-resolution wiring diagrams for entire insect nervous systems, raisinga question that is easy to pose but rarely tested directly: does the specific wiring these datasets reveal confer afunctional advantage over a structurally-matched random control, when deployed as an actual real-time controller— or does circuit scale and connectivity pattern alone explain performance, regardless of whether the wiring isbiological? We address this with a case study: a looming-avoidance circuit (four visual projection neuron types,their strongest interneuron relays, and the descending neurons they drive) is extracted from the male Drosophilamelanogaster CNS connectome (male-cns:v1.0) and deployed as a real-time controller in two interactive evasiontasks — a planar (2D) task and a full 3D free-flight task — where it must steer a simulated agent away from fallingobstacles using only its real, synapse-weighted output. We compare the extracted circuit against a degree-and-weightmatched random rewiring of itself, isolating the effect of specific connectivity from generic circuit-scale effects. Inthe 2D task, the real circuit shows a large, statistically robust advantage over its randomized control (1.1% vs. 19.2%obstacle-contact rate over 448 trials per condition; non-overlapping 95% confidence intervals; consistent across everytested control-gain setting). In the 3D task, this advantage is not detectable (Welch’s t-test, p > 0.05 at every testedsetting, n = 24 seeds per condition), which we attribute to the 3D task’s second control axis having no connectomicbasis at all in our extraction — a boundary condition of the extraction, not evidence against the first result. Wediscuss this axis-specific pattern, the method’s limitations, and the motivating — but here untested — prospect ofconnectome-derived controllers for compute-constrained edge robotics as a direction for future work.