Motion-based prediction is sufficient to solve the aperture problem
At a glance: Author's summary
- The aperture problem is a generic conundrum for the spatio-temporal 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 feed-back and lateral interactions, the so-called "association field".
- We challenge the long-held hypothesis that a propagation defined as a motion-based prediction may solve the aperture problem. Motion-based 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 motion-based predictive coding is indeed sufficient to solve the aperture problem, without the need of ad-hoc edge detectors, a prior on slow speeds or selection process. We also found that the dynamical system exhibits many properties characteristic of low-level 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 low-level sensory system. Neural implementation of such association fields would open up new perspectives for the implementation of new computational paradigms.
- Laurent Perrinet, Guillaume S. Masson. Motion-based prediction is sufficient to solve the aperture problem. Neural Computation, 24(10):2726--50, 2012 abstract
- Laurent Perrinet, Guillaume S. Masson. Motion-based prediction is sufficient to solve the aperture problem, URL URL2 URL3 URL4 URL5 . Neural Computation, 24(10):2726--50, 2012 abstractIn low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent global percept. This is well demonstrated for the detection of motion in the aperture problem: as luminance of an elongated line is symmetrical along its axis, tangential velocity is ambiguous when measured locally. Here, we develop the hypothesis that motion-based predictive coding is sufficient to infer global motion. Our implementation is based on a context-dependent diffusion of a probabilistic representation of motion. We observe in simulations a progressive solution to the aperture problem similar to psychophysics and behavior. We demonstrate that this solution is the result of two underlying mechanisms. First, we demonstrate the formation of a tracking behavior favoring temporally coherent features independently of their texture. Second, we observe that incoherent features are explained away while coherent information diffuses progressively to the global scale. Most previous models included ad-hoc mechanisms such as end-stopped cells or a selection layer to track specific luminance-based features. Here, we have proved that motion-based predictive coding, as it is implemented in this functional model, is sufficient to solve the aperture problem. This simpler solution may give insights in the role of prediction underlying a large class of sensory computations..
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