This thesis ascribes in the field of computational neuroscience whose goal is to model complex cognitive functions by means of numerical computer simulations while getting inspiration from cerebral functioning. Contrary to a top-down approach necessitating to know an analytic expression of the function to be simulated, the chosen bottom-up approach allows to observe the emergence of a function thanks to the interaction of artificial neural populations without any prior knowledge. We first present a particular neural network type, neural fields, whose properties of robustness to noise and spatio-temporal continuity allow that emergence. In order to guide the emergence of sensorimotor transformations onto this substrate, we then present the architecture of the visual and motor systems to highlight the central role of visual attention in the realization of these functions by the brain. We then propose a functional diagram of sensorimotor transformations where the preparation of an ocular saccade guides attention towards a region of visual space and allow movement preparation. We last describe a computational model of attentional spotlight displacement that, by using a dynamical spatial working memory, allows sequential search of a target in a visual scene thanks to the phenomenon of inhibition of return. The performances of this model (robustness to noise, to object movement and to saccade execution) are analyzed in simulation and on a robotic platform.