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Kameliya Dimova, Michael Denham; A neurally plausible model of the dynamics of motion integration in smooth eye pursuit based on recursive Bayesian estimation. Journal of Vision 2008;8(17):50. doi: 10.1167/8.17.50.
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In this study, we describe a model of motion integration in smooth eye pursuit based on a recursive Bayesian estimation process, which displays a dynamic behaviour qualitatively similar to the dynamics of the motion integration process observed experimentally, both psychophysically in humans and monkeys, and physiologically in monkeys (Wallace et al , 2005; Pack and Born 2001; Pack et al, 2004; Born et al, 2006). By formulating the model as an approximate version of a Kalman filter algorithm, we have been able to show that it can be put into a neurally plausible, distributed recurrent form which coarsely corresponds to the recurrent circuitry of visual cortical areas V1 and MT. The model thus provides further support for the notion that the motion integration process is based on a form of Bayesian estimation, as has been suggested by many psychophysical and other modelling studies (Weiss et al, 2002), and moreover suggests that the observed dynamic properties of this process are the result of the recursive nature of the motion estimation.
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