The machinery behind Ocular Following Response (OFR) is confronted to ambiguities which are efficiently resolved in the primate visual system. We study here a model of center-surround motion integration in a probabilistic framework and try to identify its different dynamical components by using contrast gain responses to the particular bipartite stimuli. Motion integration may be modeled as an ideal observer in a probabilistic framework by using bayesian modeling [Weiss et al., 2002] or statistical inference which proved to be successfully applied to the ocular following response [Perrinet et al., 2005]. Experiments on primates’ oculo-motor recordings concentrated on bipartite stimuli optimized to study the dynamics of information integration for different levels of noise which provide evidence for an orientation selective suppressive effect of the surround on the contrast gain control of local stimuli [Barthélemy et al., 2006]. We extend here our previous model to integrate different spatial cues: the information propagates to give a command response for ocular response that we could compare with the human behavioral response. We present results which show that the hypothesis of independence of local measures succesfully accounts for the monotonic integration of the spatial motion signal but that another mechanism should be added to account for suppressive saturation. Adding this, we observed similar dynamics for the contrast gain control mechanisms observed in the behavioral data and in neuro-physiological through in-vivo cortical recording by optical imaging (see accompanying poster number 21 by Reynaud et al. [2006]).


Figure 1: Effects of size of a disk grating on the monkey's OFR response. We present here the gain of the oculo-motor response to a central grating (temporal frequency 10 Hz) as a function of its diameter for the macaque monkey (open circles) and the model (continuous line). Fits were performed as a function of the diameter d thanks to equations derived in the text. (Left) At low frequencies (0.12~cpd) and contrasts, the gain increases monotonically with the diameter. The curves are well fitted by the simpler equation with only an excitatory pooling. (Right) However in more general conditions (here 0.7~cpd), the initial gain decreases after a given diameter suggesting a suppressive effect. This corresponds to a surround inhibition which is well captured by the equation, the inhibition being more pronounced when contrast is higher and contrary to intuition, the diameter $\omega$ extracted from the fits remains constant across curves. This provided a general explanation of the ratio-Of-Guassian model in a probabilistic framework.


additional information

see accompanying poster no 21 :

All material (c) L. Perrinet. Please check the copyright notice.

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