Connectome wiring shapes population-level neural geometry in the Drosophila visual system

What does biological wiring actually contribute to neural computation? Behavioral experiments can test whether a model produces the right outputs, but they cannot determine whether its internal representations are biologically faithful. Brunton et al. (2026) made this concrete: a C. elegans worm connectome trained with deep reinforcement learning produces realistic Drosophila fly walking -- yet the model is biologically meaningless, because behavioral fidelity is achievable without biological fidelity. We need a population-level metric that discriminates real biological wiring from arbitrary wiring, without requiring a behavioral decoder. We propose representational geometry as that metric. Representational geometry -- the structure of pairwise distances between population responses to different stimuli -- captures how a neural circuit organizes its representational space, independently of what behavior it drives. We apply representational similarity analysis (RSA) and centered kernel alignment (CKA) to the Flyvis pretrained Drosophila melanogaster visual system ensemble (Lappalainen et al. 2024): 50 networks whose architecture is fixed to the FlyWire connectome, compared against stability-constrained random baselines (sign-preserving weight shuffles, rejection-sampled for dynamic stability, n = 50). Connectome-constrained networks produce a smooth circular direction geometry that random networks cannot replicate: RSA Spearman r = 0.686 (p < 0.0001) for ON edge stimuli and r = 0.846 (p < 0.0001) for ON+OFF edge stimuli, corroborated by CKA (p < 0.05 in both experiments). The geometry also tracks biological T4/T5 direction tuning recorded in living flies (Maisak et al. 2013): connectome-constrained geometry matches biology substantially better than random geometry (r = 0.930 vs. r = 0.603, gap {Delta}r = 0.327, p < 0.0001). Within each stimulus polarity, the ON pathway encodes direction with stronger geometric separation than the OFF pathway ({Delta}r = 0.138, 95% CI [0.091, 0.236]), consistent with known T4/T5 asymmetries in direction selectivity strength. These results establish representational geometry as a candidate fidelity metric that discriminates biological from arbitrary wiring at the population level. The framework requires no behavioral decoder and no single-unit recordings--only population responses to a structured stimulus set -- suggesting a practical path toward verifiable fidelity metrics for connectome-scale emulations as they scale toward mammalian cortex.