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arXiv Computer Vision By Aashish Dhawan

FlyHack: Stress-Testing Mechanistic Claims in a Connectome-Constrained Visual Model

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FlyHack examines how mechanistic claims in a connectome‑constrained visual model depend on the comparison model, intervention, circuit representation, and trained solution. Using a 50‑model ensemble of the Drosophila motion‑vision model (flyvis) and task‑matched causal CNN controls, the study finds that behavioral phenomena such as reverse‑phi reversal and stationary‑sawtooth responses vary with period, velocity, and model family, while adversarial robustness differs between flyvis and CNNs. At the mechanistic level, attribution accuracy and circuit conclusions also shift with representation choices and ensemble members, indicating that conclusions drawn from connectome‑constrained models are highly context‑dependent.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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FlyHack evaluates how mechanistic conclusions drawn from connectome-constrained neural networks depend on model choice, interventions, circuit representation, and training outcomes. Using a 50‑model ensemble of the flyvis motion‑vision system and task‑matched causal CNN controls, the study finds that key behavioral phenomena—such as reverse‑phi reversal and stationary‑sawtooth responses—vary across models and are not consistently reproduced by CNNs. At the mechanistic level, attribution accuracy and circuit conclusions also differ markedly with representation and ensemble member, indicating that mechanistic claims must be scoped to the specific model family and experimental conditions.

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