Inferring monkey ocular following responses from V1 population dynamics using a probabilistic model of motion integration

FIGURE 1: Experimental setup and probabilistic Ratio-Of-Gaussian (pROG) model. Short presentation of a large moving pattern elicits an Ocular Following Response (OFR) that exhibits many of the properties attributed to low-level motion processing such as spatial and temporal integration, contrast gain control and divisive interaction between competing motions. We recorded simultaneously V1 population dynamics using Voltage Sensitive Dye Imaging (VSDI) and eye movement using the scleral search coil technique (see accompagnying poster [Reynaud et al., 2009]). To link both recordings, we use the probabilistic Ratio-Of-Gaussian (pROG) model. It extends the ideal observer model [Weiss et al., 2002] in the dynamical domain to model the spatial integration of the different local motion cues within a probabilistic representation. To model spatial integration, we considered first the density of neurons pooling responses for the OFR as a centered Gaussian along with an inhibition as a centered and broader Gaussian: these hypothesis lead to the Ratio-Of-Gaussian (ROG) model [Sceniak et al., 1999; Cavanaugh et al., 2002]. We proved that this model is successfully adapted to model the OFR for the different experiments, that is for different levels of noise with full field gratings, with disks of various sizes and also for the effect of a flickering surround [Perrinet and Masson, 2007]. Using the simultaneous recordings of VSDI and OFR, our objective is to validate the model and derive the most likely set of weights for the center and surround. Center-surround interactions in OFR & VSDI