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
The study investigates whether a biological wiring diagram can serve as a computational substrate for tasks beyond its evolutionary purpose. Using the MaleCNS v1.0 Drosophila connectome as a fixed recurrent network, the authors train models to perform bounded addition and a grounded relational language task, achieving high accuracy (92.77% for addition and 61.59% for language) that surpasses degree-preserving rewired controls. The results demonstrate that the higher-order structure of the MaleCNS provides a reusable inductive bias for these cognitive tasks.
By Joonghui Cho, Minchan Kang, Daeshik Kim
Can a biological wiring diagram serve as a useful computational substrate beyond the behaviors for which it evolved? We use the publicly released MaleCNS v1.0 connectome, reconstructed from a single a...
The paper introduces a framework for defining and certifying computational sufficiency in neural systems constrained by a connectome. It formalises sufficiency as a cost-indexed family of neuron subsets conditioned on a task and a tolerance pair, and certifies candidate circuits using one-sided bounds over held-out parameter draws. Experiments on a walking simulator demonstrate that the published 148-neuron circuit is uniquely sufficient, while variations in task or tolerance yield different sufficient subsets, highlighting that sufficiency is set-valued and task-dependent.
By Ali Mahdavi, Azadeh Zamanifar, Amirfarhad Farhadi, Omid Kashefi
SYNAPSE is a training‑free framework that probes Transformer models by extracting per‑layer [CLS] representations, ranking neurons with a lightweight linear probe, and applying forward‑hook interventions during inference. It enables controlled, repeatable experiments on internal representations without altering the original model, revealing that task‑relevant information is encoded in broad, overlapping neuron subsets and that small weight or logit manipulations can redirect predictions. The framework highlights functional stability, class‑specific specialization, and complementary vulnerability profiles across domains.
By Jes\'us S\'anchez Ochoa, Enrique Tom\'as Mart\'inez Beltr\'an, Alberto Huertas Celdr\'an
arXiv:2606. 09902v1 Announce Type: cross Abstract: Reservoir computing exploits the fixed dynamics of a recurrent network for temporal processing, requiring only a trained linear readout.
By Anmol Guragain, Savvas Kakalis, Juan Ignacio Godino-Llorente
arXiv:2609.24565v1 Announce Type: new
Abstract: Architectural drawings encode material classes through repeated hatch patterns. We test whether a connectome-constrained fly visual network, pretrained...
By Dmitry Kuklev