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基于运动的预测足以解决孔径问题。

Motion-based prediction is sufficient to solve the aperture problem.

机构信息

Institut de Neurosciences de la Timone, CNRS/Aix-Marseille University 13385 Marseille Cedex 5, France.

出版信息

Neural Comput. 2012 Oct;24(10):2726-50. doi: 10.1162/NECO_a_00332. Epub 2012 Jun 26.

DOI:10.1162/NECO_a_00332
PMID:22734489
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3472550/
Abstract

In 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 physiology 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 independent 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 as necessary conditions to solve the aperture problem. 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 solution may give insights into the role of prediction underlying a large class of sensory computations.

摘要

在低水平感觉系统中,仍不清楚神经元局部采集的嘈杂信息如何产生一致的全局感觉。这在孔径问题中的运动检测中得到了很好的证明:由于细长线的亮度沿其轴是对称的,因此局部测量时切向速度是不确定的。在这里,我们提出了基于运动的预测编码足以推断全局运动的假设。我们的实现基于运动概率表示的上下文相关扩散。我们在模拟中观察到类似于生理学和行为的孔径问题的渐进解决方案。我们证明,这个解决方案是两个潜在机制的结果。首先,我们证明了跟踪行为的形成,该行为有利于独立于其纹理的时间上一致的特征。其次,我们观察到不连贯的特征被解释掉了,而连贯的信息逐渐扩散到全局范围。大多数先前的模型都包含了一些特定机制,如端停细胞或选择层,以跟踪特定基于亮度的特征,作为解决孔径问题的必要条件。在这里,我们已经证明了基于运动的预测编码,就像在这个功能模型中实现的那样,足以解决孔径问题。这个解决方案可能会深入了解预测在一大类感觉计算中的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d748/3472550/5fa95cfeb865/halms726856f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d748/3472550/6dac80f090ee/halms726856f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d748/3472550/21141152a7bd/halms726856f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d748/3472550/f048634735d7/halms726856f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d748/3472550/5fa95cfeb865/halms726856f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d748/3472550/6dac80f090ee/halms726856f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d748/3472550/21141152a7bd/halms726856f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d748/3472550/f048634735d7/halms726856f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d748/3472550/5fa95cfeb865/halms726856f4.jpg

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