  We address the functional role of 'feature inhibition' in vision models; that
is, what are the mechanisms by which a neural network ensures images do not
express a given feature? We observe that standard interpretability tools in the
literature are not immediately suited to the inhibitory case, given the
asymmetry introduced by the ReLU activation function. Given this, we propose
inhibition be understood through a study of 'maximally tense images' (MTIs),
i.e. those images that excite and inhibit a given feature simultaneously. We
show how MTIs can be studied with two novel visualization techniques; +/-
attribution inversions, which split single images into excitatory and
inhibitory components, and the attribution atlas, which provides a global
visualization of the various ways images can excite/inhibit a feature. Finally,
we explore the difficulties introduced by superposition, as such interfering
features induce the same attribution motif as MTIs.
