  While some convolutional neural networks (CNNs) have achieved great success
in object recognition, they struggle to identify objects in images corrupted
with different types of common noise patterns. Recently, it was shown that
simulating computations in early visual areas at the front of CNNs leads to
improvements in robustness to image corruptions. Here, we further explore this
result and show that the neuronal representations that emerge from precisely
matching the distribution of RF properties found in primate V1 is key for this
improvement in robustness. We built two variants of a model with a front-end
modeling the primate primary visual cortex (V1): one sampling RF properties
uniformly and the other sampling from empirical biological distributions. The
model with the biological sampling has a considerably higher robustness to
image corruptions that the uniform variant (relative difference of 8.72%).
While similar neuronal sub-populations across the two variants have similar
response properties and learn similar downstream weights, the impact on
downstream processing is strikingly different. This result sheds light on the
origin of the improvements in robustness observed in some biologically-inspired
models, pointing to the need of precisely mimicking the neuronal
representations found in the primate brain.
