Within the central nervous system, visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about the environment. This process is constrained by the necessity of being robust and rapid. Indeed, there exists both a wide variety of potential changes in the geometrical characteristics of the visual scene and also a necessity to be able to respond as quickly as possible to the incoming sensory stream, for instance to drive a movement of the eyes to the location of a potential danger. Decades of study in neurophysiology and psychophysics at the different levels of vision have shown that this system takes advantage of a priori knowledge about the structure of visual information, such as the regularity in the shape and motion of visual objects. As such, the predictive processing framework offers a unified theory to explain a variety of visual mechanisms. However, we still lack a global normative approach unifying those mechanisms and we will review here some recent and promising approaches. First, we will describe Active Inference, a form of predictive processing equipped with the ability to actively sample the visual space. Then, we will extend this paradigm to the case where information is distributed on a topography, such as is the case for retinotopically organized visual areas. In particular, we will compare such models in light of recent neurophysiological data showing the role of traveling waves in shaping visual processing. Finally, we will propose some lines of research to understand how these functional models may be implemented at the neural level. In particular, we will review potential models of cortical processing in terms of prototypical micro-circuits. These allow to separate the different flows of information, from feed-forward prediction error to feed-back anticipation error. Still, the design of such a generic predictive processing circuit is still not fully understood and we will enumerate some possible implementations using biomimetic neural networks.