@ Universität Ulm - Heiko Neumann's lab
- Laurent Perrinet, Guillaume S. Masson. Decoding the population dynamics underlying ocular following responseusing a probabilistic framework, URL . 2008 abstractThe machinery behind the visual perception of motion and the subsequentsensorimotor transformation, such as in Ocular Following Response (OFR),is confronted to uncertainties which are efficiently resolved in theprimate’s visual system. We may understand this response as an idealobserver in a probabilistic framework by using Bayesian theory (Weiss etal., 2002) which we previously proved to be successfully adapted tomodel the OFR for different levels of noise with full field gratings orwith disk of various sizes and the effect of a flickering surround(Perrinet and Masson, 2007).More recent experiments of OFR have used disk gratings and bipartitestimuli which are optimized to study the dynamics of center-surroundintegration. We quantified two main characteristics of the globalspatial integration of motion from an intermediate map of possible localtranslation velocities: (i) a finite optimal stimulus size for drivingOFR, surrounded by an antagonistic modulation and (ii) a directionselective suppressive effect of the surround on the contrast gaincontrol of the central stimuli (Barthelemy et al., 2006, 2007).Herein, we extended in the dynamical domain the ideal observer model tosimulate the spatial integration of the different local motion cueswithin a probabilistic representation. We present analytical resultswhich show that the hypothesis of independence of local measures candescribe the initial segment of spatial integration of motion signal.Within this framework, we successfully accounted for the dynamicalcontrast gain control mechanisms observed in the behavioral data forcenter-surround stimuli. However, another inhibitory mechanism had to beadded to account for suppressive effects of the surround. We explorehere an hypothesis where this could be understood as the effect of arecurrent integration of information in the velocity map.F. Barthelemy, L. U. Perrinet, E. Castet, and G. S. Masson. Dynamics ofdistributed 1D and 2D motion representations for short-latency ocularfollowing. Vision Research, 48(4):501–22, feb 2007. doi:10.1016/j.visres.2007.10.020.F. V. Barthelemy, I. Vanzetta, and G. S. Masson. Behavioral receptivefield for ocular following in humans: Dynamics of spatial summation andcenter-surround interactions. Journal of Neurophysiology,(95):3712–26, Mar 2006. doi: 10.1152/jn.00112.2006.L. U. Perrinet and G. S. Masson. Modeling spatial integration in theocular following response using a probabilistic framework. Journal ofPhysiology (Paris), 2007. doi: 10.1016/j.jphysparis.2007.10.011.Y. Weiss, E. P. Simoncelli, and E. H. Adelson. Motion illusions asoptimal percepts. Nature Neuroscience, 5(6):598–604, Jun 2002. doi:10.1038/nn858..
This work was supported by European integrated project FP6-015879, "FACETS".