  The study of the visual system of the brain has attracted the attention and
interest of many neuro-scientists, that derived computational models of some
types of neuron that compose it. These findings inspired researchers in image
processing and computer vision to deploy such models to solve problems of
visual data processing. In this paper, we review approaches for image
processing and computer vision, the design of which is based on
neuro-scientific findings about the functions of some neurons in the visual
cortex. Furthermore, we analyze the connection between the hierarchical
organization of the visual system of the brain and the structure of
Convolutional Networks (ConvNets). We pay particular attention to the
mechanisms of inhibition of the responses of some neurons, which provide the
visual system with improved stability to changing input stimuli, and discuss
their implementation in image processing operators and in ConvNets.
