  High-level visual brain regions contain subareas in which neurons appear to
respond more strongly to examples of a particular semantic category, like faces
or bodies, rather than objects. However, recent work has shown that while this
finding holds on average, some out-of-category stimuli also activate neurons in
these regions. This may be due to visual features common among the preferred
class also being present in other images. Here, we propose a
deep-learning-based approach for visualizing these features. For each neuron,
we identify relevant visual features driving its selectivity by modelling
responses to images based on latent activations of a deep neural network. Given
an out-of-category image which strongly activates the neuron, our method first
identifies a reference image from the preferred category yielding a similar
feature activation pattern. We then backpropagate latent activations of both
images to the pixel level, while enhancing the identified shared dimensions and
attenuating non-shared features. The procedure highlights image regions
containing shared features driving responses of the model neuron. We apply the
algorithm to novel recordings from body-selective regions in macaque IT cortex
in order to understand why some images of objects excite these neurons.
Visualizations reveal object parts which resemble parts of a macaque body,
shedding light on neural preference of these objects.
