  To date, most discoveries of network subcomponents that implement
human-interpretable computations in deep vision models have involved close
study of single units and large amounts of human labor. We explore scalable
methods for extracting the subgraph of a vision model's computational graph
that underlies recognition of a specific visual concept. We introduce a new
method for identifying these subgraphs: specifying a visual concept using a few
examples, and then tracing the interdependence of neuron activations across
layers, or their functional connectivity. We find that our approach extracts
circuits that causally affect model output, and that editing these circuits can
defend large pretrained models from adversarial attacks.
