# Circuit discovery

Features inside a neural network are often carried by groups of neurons acting together, and a circuit drawn as pairwise edges misses the joint effect of suppressing such a group. The new construction treats the network as a structural causal model, uses counterfactual ablations that stay on the neural manifold to progressively suppress a set of neurons, and admits neuron groups into a directed simplicial complex by a statistical relevance test. On two small handcrafted multilayer perceptrons the complexes recover the designed feature pathways, and their 1-skeletons agree with the edges kept by ACDC, EAP and EAP-IG that a weight graph can express.

Features inside a neural network are often carried by groups of neurons acting together, and a circuit drawn as pairwise edges misses the joint effect of suppressing such a group. The new construction treats the network as a structural causal model, uses counterfactual ablations that stay on the neural manifold to progressively suppress a set of neurons, and admits neuron groups into a directed simplicial complex by a statistical relevance test. On two small handcrafted multilayer perceptrons the complexes recover the designed feature pathways, and their 1-skeletons agree with the edges kept by ACDC, EAP and EAP-IG that a weight graph can express.

Stress-testing the 50 trained solutions of the fly vision model flyvis shows that a connectome constraint does not make mechanistic conclusions unique: reverse-phi reversal also appears in task-matched causal CNNs, attribution effect sizes agree with exhaustive single-connection ground truth only when matched to the intervention, and only 25 of 50 models meet a T4/T5 tuning criterion. The value of the constraint is that mechanistic hypotheses become directly testable, not that a mechanism is unique.

Stress-testing the 50 trained solutions of the fly vision model flyvis shows that a connectome constraint does not make mechanistic conclusions unique: reverse-phi reversal also appears in task-matched causal CNNs, attribution effect sizes agree with exhaustive single-connection ground truth only when matched to the intervention, and only 25 of 50 models meet a T4/T5 tuning criterion. The value of the constraint is that mechanistic hypotheses become directly testable, not that a mechanism is unique.