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 Flyvis connectome (reconstructed from partial electron-microscopy sources), compared against stability-constrained random baselines (sign-preserving weight shuffles, rejection-sampled for dynamic stability, n = 50).
Zhou, M. G. et al. · CC-BY 4.0