  Understanding the nature of representation in neural networks is a goal
shared by neuroscience and machine learning. It is therefore exciting that both
fields converge not only on shared questions but also on similar approaches. A
pressing question in these areas is understanding how the structure of the
representation used by neural networks affects both their generalization, and
robustness to perturbations. In this work, we investigate the latter by
juxtaposing experimental results regarding the covariance spectrum of neural
representations in the mouse V1 (Stringer et al) with artificial neural
networks. We use adversarial robustness to probe Stringer et al's theory
regarding the causal role of a 1/n covariance spectrum. We empirically
investigate the benefits such a neural code confers in neural networks, and
illuminate its role in multi-layer architectures. Our results show that
imposing the experimentally observed structure on artificial neural networks
makes them more robust to adversarial attacks. Moreover, our findings
complement the existing theory relating wide neural networks to kernel methods,
by showing the role of intermediate representations.
