Computational Neuroscience is a synthetic, inter-disciplinary approach aiming at understanding cognition by analyzing the mechanisms underlying neural computations. We present in this seminar our attempt in modeling low-level vision by bridging different integration levels, from neural spiking activity to behavior. At the behavioral level, the Ocular Following Response recorded in the laboratory reveals how the brain may integrate local information (moving images on visual receptive fields) to produce a single behavioral response (the movement of the eye). Using a probabilistic representation, we provide a simple integrative mechanism that gives the ‘‘ideal’’ response to possibly noisy and ambiguous information, similarly to a Bayesian approach. This fits well the performance revealed by behavioral data and may act as a generic cortical ‘‘module’'. At the population level, these mechanisms may indeed be implemented for the coding of natural images and we will show the particular importance of spiking representations and lateral interactions for efficient and rapid responses. In particular, we will present an original unsupervised learning algorithm that we applied to a model of the primary visual cortex. Finally, at the neuronal level, I will present work done in the team showing how certain mechanisms at the level of the synapse and of the neuron are essential at the population level and how we may understand these mechanisms at the population level. This illustrates the importance of dynamical processes, distributed activity and recurrent connections to produce a cortical gain control mechanism. As a conclusion, this approach provides useful applications for image processing and possible valorization in future computer architectures. More generally, it proves that the use of a probabilistic representation is a particularly efficient method for bridging biological versus computational neuroscience and illustrates the advantage of such an interdisciplinary approach.