How the fruit fly connectome can help autonomous materials discovery

Autonomous laboratories for two-dimensional semiconductors pair a recipe planner with instruments that return in situ spectra every few seconds. Some decisions are too fast for the planner: stop a failing selenization anneal, flag a flake that is not the logged material, or end an ion irradiation at a set vacancy density, from a few examples, on a small computer at the instrument. The fruit fly takes decisions of this form with two structures whose wiring is measured at synapse level: the mushroom body, a one-shot memory for odours, and the ventral nerve cord, which turns a few descending commands into local reflexes. We asked which instrument jobs models wired from these connectomes do better than engineered tools. A mushroom-body model of 685 projection neurons and 5,177 Kenyon cells read one Raman band per glomerulus; over 20 paired seeds, on simulated selenization Raman streams and on Ar$^+$ irradiation anchored in published in situ spectra, it lost the fewest flakes when one failed run taught a stop rule (3.3 of 30, against 3.9 for a nearest neighbour), read vacancy density from its sparse code with less error than from the raw spectrum (5.5 % against 6.4 %), and ran in 0.30 ms on a Raspberry Pi 5 within 152 kB. A stop decided at the furnace, as the nerve cord decides reflexes, prevented the losses of a planner that needed 60 s per turn (0.15 against 10.7 flakes of 30). It did not choose recipes. In three animals and after training, measured wiring matched a degree-preserving shuffle, and the nerve-cord wiring matched a ridge regression: the connectome contributes an architecture and wiring statistics, not a partner map. As hardware, the fixed wiring can be binary and each plastic synapse needs about 64 levels. The connectome supplies a low-cost reflex layer under the planner, not a replacement.