We present a simple mathematical model for the mammalian low visual pathway,
taking into account its key elements: retina, lateral geniculate nucleus (LGN),
primary visual cortex (V1). The analogies between the cortical level of the
visual system and the structure of popular CNNs, used in image classification
tasks, suggests the introduction of an additional preliminary convolutional
module inspired to precortical neuronal circuits to improve robustness with
respect to global light intensity and contrast variations in the input images.
We validate our hypothesis on the popular databases MNIST, FashionMNIST and
SVHN, obtaining significantly more robust CNNs with respect to these
variations, once such extra module is added.

研究发现，在哺乳动物的低级视觉通路中，加入一种基于前皮层神经元回路的卷积模块可以显著提高卷积神经网络的鲁棒性。

用于光变稳健卷积神经网络的前皮层模块

A precortical module for robust CNNs to light variations

Orientation selectivity is the most striking feature of simple cell coding in
V1 which has been shown to emerge from the reduction of higher-order
correlations in natural images in a large variety of statistical image models.
The most parsimonious one among these models is linear Independent Component
Analysis (ICA), whereas second-order decorrelation transformations such as
Principal Component Analysis (PCA) do not yield oriented filters. Because of
this finding it has been suggested that the emergence of orientation
selectivity may be explained by higher-order redundancy reduction. In order to
assess the tenability of this hypothesis, it is an important empirical question
how much more redundancies can be removed with ICA in comparison to PCA, or
other second-order decorrelation methods. This question has not yet been
settled, as over the last ten years contradicting results have been reported
ranging from less than five to more than hundred percent extra gain for ICA.
Here, we aim at resolving this conflict by presenting a very careful and
comprehensive analysis using three evaluation criteria related to redundancy
reduction: In addition to the multi-information and the average log-loss we
compute, for the first time, complete rate-distortion curves for ICA in
comparison with PCA. Without exception, we find that the advantage of the ICA
filters is surprisingly small. Furthermore, we show that a simple spherically
symmetric distribution with only two parameters can fit the data even better
than the probabilistic model underlying ICA. Since spherically symmetric models
are agnostic with respect to the specific filter shapes, we conlude that
orientation selectivity is unlikely to play a critical role for redundancy
reduction.

研究表明，在视觉皮层 V1 中，方向选择性源于自然图像中高阶冗余关联的降低，而线性独立成分分析模型 (ICA) 能够比主成分分析 (PCA) 更好地去除这种冗余，但我们发现 ICA 滤波器的优势非常小，并且简单的球对称分布可以比 ICA 更好地解释这一现象。因此，我们推断方向选择性对于冗余的减少不太可能起到关键作用。