Connectome wiring sometimes loses when physiology tuning varies
When Does the Real Wiring Win? Testing connectome structure-function claims against unmeasured physiology
Machine Learning
Summary
Understanding how the brain's wiring influences its function is tricky because models often mix wiring with other factors like neuron behavior settings. The authors carefully tested when a real brain circuit’s wiring truly performs better than random alternatives by checking how different tuning methods affect results. Sometimes, the actual wiring does better; other times, it doesn't once more realistic neuron properties are included. This work shows that to fairly claim wiring drives function, models must be tuned similarly and use data-constrained physiology.
What this means in practice
- •For computational neuroscientists: Test neural circuit models with matched parameter tuning to avoid misleading structure-function conclusions.
- •For neural data analysts: Use data-constrained physiological parameters when comparing real and shuffled wiring in brain circuit analysis.
Authors
Yeyuan Liu
Abstract
Connectome-constrained models are often used to argue that a measured wiring diagram outperforms shuffled or rewired wiring, and hence that structure shapes function. Such comparisons can confound wiring with how the models are tuned: a connectome fixes who connects to whom, not the excitability, gains or plasticity rates the model also needs, and modellers often tune these until the real-wiring model works. We separate three estimands: physiology tuned on the real wiring and transferred to nulls (P1), each wiring tuned on its own (P2), and P2 restricted to physiology consistent with independent data (P3). The biases involved are known in machine learning (under-tuned baselines, inherited weights, winner's curse); we measure what they do to biological conclusions, using synthetic ground truth, placebo nulls and three Drosophila circuit models. Some claims survive every protocol we test: segregated reward and punishment dopamine improves learning, and head-direction topography is required for a persistent bump. Others do not. In the mushroom body, the real Kenyon-cell output wiring beats a cross-subtype identity shuffle by 0.09 under P1 and P2 but loses to it by 0.09 under P3 (95% CIs exclude zero; held-out runs confirm). The verdict is set by the share of alpha'beta' cells among active Kenyon cells, which P1 selects low; at the share implied by imaging (about 44%) the real-wiring model fails to learn, exposing a model inadequacy rather than a deficit of the fly's wiring. In flyvis, P1 penalises a mirrored connectome, which has a constructed solution within 0.07 (median) of the real error, more than a spatially shuffled one in 50 of 50 members; the ranking survives ten-fold longer retraining but depends on the checkpoint at fifty-fold. A credible structural claim needs matched optimisation, data-constrained physiology and a model that still works there.