Figure 2 To model spatial integration in the OFR in primates (humans and macaques), we use the tools of statistical inference with the hypothesis that information is represented in a probabilistic fashion. The architecture of the OFR system consists in this model of a stage extracting from the raw image the possible local translation velocities (V1) to represent the local probabilities of translational velocity (MT). These local bits of information are then pooled (MST) to give a single probabilistic representation of possible translational velocities to the oculomotor system, which then controls the eyes' motion as quickly and efficiently as possible to stabilize the image on the retina. The local probabilities may be often described as gaussian probabilities, and the gain response as a function to the signal to noise ratio is then given by a Naka-Rushton curve (Barthélemy et al., 2007).

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