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首页> 外文期刊>Current Biology: CB >'What Not'' Detectors Help the Brain See in Depth
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'What Not'' Detectors Help the Brain See in Depth

机译：“什么不是”探测器有助于大脑深入了解

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摘要

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 disparitydetection 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.
机译：双目立体镜是从昆虫到灵长类动物的物种中三维（3D）视觉的主要提示之一。了解从两种不同视网膜图像的大脑提取深度是如何在感觉神经科学中的一种易攻击，这迄今为止已经疏散了完整的解释。当前思维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”），以便世界上的物体深度可以是三角形的。虽然直观，这种方法未能考虑关键的生理和感知观察。我们展示了识别“正确匹配”的问题是次优的，并提出了一种基于最佳信息编码的替代方案，该方法与“申请”的“撤销”：利用不同的特征反对不太可能的解释的证据。我们展示了这些“什么不是”在优化以提取自然图像深度的神经网络中的反应。网络将信息与对所观看的场景的可能深度结构相结合，自然再现神经响应和感知解释的关键特征。我们以简单的分析形式捕获网络的编码和读数计算，并导出双目似乎模型，其在生理和感知水平的3D视觉中提供了一个统一的长期谜题。我们建议用释放嫁接检测为感官估计提供了有效的编码策略，这对不同特征域（例如，运动）和多福管集成有用。

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