To move in a complex and changing environment, according to the active perception theory, an agent needs to learn the multi modal correlations between its actions and the changes they induce in the environment . Human beings interact with their environment through several distant organs, whose sensory flow processing are influencing on each others as, for example, in the Mc Gurk effect. In the functional view of the cortex, each sensory area processes a specific sensory flow and associative areas integrate these flows in a consistent representation of the world that influences in return uni modal perceptions as for the ventriloquist effect. At a mesoscopic level, the cortex shows a generic structure composed of multi layers cortical columns. We propose a bio inspired model of perceptive map which, using a continuous and unsupervised learning, self-organizes to map a sensory data flow. Thus, using a spatial competition mechanism, a perceptive map provides an activity bump representing the current perception. In a multi modal architecture, by connecting multiple perceptive maps to an associative map with reciprocal spatially constrained connectivity, the spatial localization of each perception have to relax the multi modal constraints.