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.
By Aashish Dhawan
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.
arXiv:2610.10023v1 Announce Type: cross
Abstract: Understanding the extent to which measured synaptic wiring determines computation remains a central challenge. Here, we couple the proofread adult Dr...
By Eudald Correig-Fraga, Roger Guimer\`a, Marta Sales-Pardo
arXiv:2610.08418v1 Announce Type: new
Abstract: Biological connectomes encode structured solutions to visual computation that may provide reusable inductive biases for artificial vision. We develop C...
By Zongyu Li, Akito Yamauchi, Huaizhi Liu, Vishwanatha Rao, Jia Guo, for the Frontotemporal Lobar Degeneration Neuroimaging Initiative, for the Alzheimer's Disease Neuroimaging Initiative
arXiv:2609.24565v2 Announce Type: replace
Abstract: A connectome-constrained model of the fly visual system, optimized for motion and then frozen, can be driven over architectural drawings by prescri...
By Dmitry Kuklev
arXiv:2607. 00025v1 Announce Type: cross Abstract: While deep learning models achieve state-of-the-art performance in complex tasks, they remain brittle when faced with new environments or sensory deprivation.
By Benquan Wang, Jingdao Chen