Title: A precortical module for robust CNNs to light variations. Abstract: 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. 