Modeling spatial integration in the ocular following response using a probabilistic framework


The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in Ocular Following Response (OFR), is confronted to uncertainties which are efficiently resolved in the primate’s visual system. We may understand this response as an ideal observer in a probabilis- tic framework by using Bayesian theory (Weiss et al., 2002) which we previously proved to be successfully adapted to model the OFR for different levels of noise with full field gratings (Perrinet et al., 2005). More recent experiments of OFR have used disk gratings and bipartite stimuli which are optimized to study the dy- namics of center-surround integration. We quantified two main characteristics of the spatial integration of motion : (i) a finite optimal stimulus size for driving OFR, surrounded by an antagonistic modulation and (ii) a direction selective sup- pressive effect of the surround on the contrast gain control of the central stim- uli (Barthélemy et al., 2006). Herein, we extended the ideal observer model to simulate the spatial integration of the different local motion cues within a proba- bilistic representation. We present analytical results which show that the hypoth- esis of independence of local measures can describe the integration of the spatial motion signal. Within this framework, we successfully accounted for the con- trast gain control mechanisms observed in the behavioral data for center-surround stimuli. However, another inhibitory mechanism had to be added to account for suppressive effects of the surround.

Journal of Physiology-Paris


Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.