  We introduce a novel computational unit, termed PushPull-Conv, in the first
layer of a ResNet architecture, inspired by the anti-phase inhibition
phenomenon observed in the primary visual cortex. This unit redefines the
traditional convolutional layer by implementing a pair of complementary
filters: a trainable push kernel and its counterpart, the pull kernel. The push
kernel (analogous to traditional convolution) learns to respond to specific
stimuli, while the pull kernel reacts to the same stimuli but of opposite
contrast. This configuration enhances stimulus selectivity and effectively
inhibits response in regions lacking preferred stimuli. This effect is
attributed to the push and pull kernels, which produce responses of comparable
magnitude in such regions, thereby neutralizing each other. The incorporation
of the PushPull-Conv into ResNets significantly increases their robustness to
image corruption. Our experiments with benchmark corruption datasets show that
the PushPull-Conv can be combined with other data augmentation techniques to
further improve model robustness. We set a new robustness benchmark on ResNet50
achieving an $mCE$ of 49.95$\%$ on ImageNet-C when combining PRIME augmentation
with PushPull inhibition.
