Motionbased prediction is sufficient to solve the aperture problem

At a glance: Author's summary
 The aperture problem is a generic conundrum for the spatiotemporal integration and binding of sensory information from the local to global scales. It is believed that its neural solution originates from the recursive propagation implemented by finely tuned feedback and lateral interactions, the socalled "association field".
 We challenge the longheld hypothesis that a propagation defined as a motionbased prediction may solve the aperture problem. Motionbased prediction is defined as the prediction that motion follows smooth trajectories, as is observed in natural scenes.
 We use probabilities as a generic framework for understanding the consequences of this hypothesis at the functional level. To overcome simulation problems, we use a simple method inherited from computer vision that gives a much more precise approximation to this complex problem compared to previous models.
Using this dynamical model, we find that motionbased predictive coding is indeed sufficient to solve the aperture problem, without the need of adhoc edge detectors, a prior on slow speeds or selection process. We also found that the dynamical system exhibits many properties characteristic of lowlevel sensory areas, both at the behavioral and neurophysiological levels.
 As a conclusion, the inclusion of such local interactions inspired by the structure of natural scenes proves to be a simple and efficient model for such a lowlevel sensory system. Neural implementation of such association fields would open up new perspectives for the implementation of new computational paradigms.

reference
 Laurent Perrinet, Guillaume S. Masson. Motionbased prediction is sufficient to solve the aperture problem. Neural Computation, 24(10):272650, 2012 abstract .
 Laurent Perrinet, Guillaume S. Masson. Motionbased prediction is sufficient to solve the aperture problem, URL URL2 URL3 URL4 URL5 . Neural Computation, 24(10):272650, 2012 abstract .
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