  Despite impressive performance on numerous visual tasks, Convolutional Neural
Networks (CNNs) --- unlike brains --- are often highly sensitive to small
perturbations of their input, e.g. adversarial noise leading to erroneous
decisions. We propose to regularize CNNs using large-scale neuroscience data to
learn more robust neural features in terms of representational similarity. We
presented natural images to mice and measured the responses of thousands of
neurons from cortical visual areas. Next, we denoised the notoriously variable
neural activity using strong predictive models trained on this large corpus of
responses from the mouse visual system, and calculated the representational
similarity for millions of pairs of images from the model's predictions. We
then used the neural representation similarity to regularize CNNs trained on
image classification by penalizing intermediate representations that deviated
from neural ones. This preserved performance of baseline models when
classifying images under standard benchmarks, while maintaining substantially
higher performance compared to baseline or control models when classifying
noisy images. Moreover, the models regularized with cortical representations
also improved model robustness in terms of adversarial attacks. This
demonstrates that regularizing with neural data can be an effective tool to
create an inductive bias towards more robust inference.
