"What Not" Detectors Help the Brain See in Depth.
"What Not" Detectors Help the Brain See in Depth.
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DOI：
10.1016/j.cub.2017.03.074
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发表时间：
2017-05-22
期刊：
影响因子：
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通讯作者：
Welchman AE
中科院分区：
文献类型：
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作者：
Goncalves NR;Welchman AE
双目立体视觉是从昆虫到灵长类动物的三维视觉的主要线索之一。理解大脑如何从两种不同的视网膜图像中提取深度，是感觉神经科学中一个容易处理的挑战，迄今为止还没有得到充分的解释。当前思想的核心是大脑需要识别两个视网膜图像中的匹配特征（即，解决“立体对应问题”），从而可以对世界中的物体的深度进行三角测量。虽然直观，这种方法无法解释关键的生理和感知观察。我们表明，制定的问题，以确定“正确的匹配”是次优的，并提出了一种替代方案，基于最佳的信息编码，混合视差检测与“proscription”：利用不同的功能提供证据，对不太可能的解释。我们展示了这些“什么不是”的反应在神经网络中的作用，该神经网络被优化为在自然图像中提取深度。该网络结合了所观察场景的可能深度结构的信息，自然地再现了神经反应和感知解释的关键特征。我们捕获的编码和读出计算的网络在简单的分析形式，并得出一个双目似然模型，提供了一个统一的帐户长期存在的难题在3D视觉在生理和感知水平。我们认为，将检测与禁止相结合为感官估计提供了一种有效的编码策略，这可能对不同的特征域（例如，运动）和多感觉整合。大脑使用“什么不是”探测器来促进3D视觉双目不匹配被用来驱动抑制不兼容的深度没有双目对应的深度感知的Proscription帐户一个简单的分析模型捕捉感知和神经反应Goncalves和Welchman表明，长期存在的3D视觉的生理学和感知的困惑可以通过大脑使用“什么不是”探测器来解释。这些通过提供证据反对与场景的真实结构不相容的解释来促进立体视觉。
Binocular stereopsis is one of the primary cues for three-dimensional (3D) vision in species ranging from insects to primates. Understanding how the brain extracts depth from two different retinal images represents a tractable challenge in sensory neuroscience that has so far evaded full explanation. Central to current thinking is the idea that the brain needs to identify matching features in the two retinal images (i.e., solving the “stereoscopic correspondence problem”) so that the depth of objects in the world can be triangulated. Although intuitive, this approach fails to account for key physiological and perceptual observations. We show that formulating the problem to identify “correct matches” is suboptimal and propose an alternative, based on optimal information encoding, that mixes disparity detection with “proscription”: exploiting dissimilar features to provide evidence against unlikely interpretations. We demonstrate the role of these “what not” responses in a neural network optimized to extract depth in natural images. The network combines information for and against the likely depth structure of the viewed scene, naturally reproducing key characteristics of both neural responses and perceptual interpretations. We capture the encoding and readout computations of the network in simple analytical form and derive a binocular likelihood model that provides a unified account of long-standing puzzles in 3D vision at the physiological and perceptual levels. We suggest that marrying detection with proscription provides an effective coding strategy for sensory estimation that may be useful for diverse feature domains (e.g., motion) and multisensory integration. The brain uses “what not” detectors to facilitate 3D vision Binocular mismatches are used to drive suppression of incompatible depths Proscription accounts for depth perception without binocular correspondence A simple analytical model captures perceptual and neural responses Goncalves and Welchman show that long-standing puzzles for the physiology and perception of 3D vision are explained by the brain’s use of “what not” detectors. These facilitate stereopsis by providing evidence against interpretations that are incompatible with the true structure of the scene.