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.

在具有卷积神经网络的目标识别方面取得了巨大成功的同时，它们难以辨识出带有不同类型常见噪音模式的图像中的对象。最近的一项研究表明，在模拟 CNN 前端的早期视觉区域中的计算将提高对图像破坏的鲁棒性，本文进一步探讨了这一结果，并显示精确匹配灵长类 V1 区中发现的 RF 特性分布所产生的神经元表示对于这种鲁棒性改进至关重要。我们构建了两种变体的模型，其中前端模型模拟了灵长类的视觉皮层（V1）：一种是均匀采样 RF 特性，另一种是采样自生物实验分布。具有生物采样的模型对图像破坏具有较高的鲁棒性（相对差异为 8.72%）。虽然两个变体之间的类似神经元亚种群具有相似的响应特性，并学习相似的下游权重，但对下游处理的影响却截然不同。这个结果揭示了一些仿生模型鲁棒性改进的原因，指向了精确模仿在灵长类大脑中发现的神经元表示的必要性。