Neural Codes Underlying Visual Segmentation

项目摘要/摘要 在自然视觉中，很少会遇到孤立的物体出现在空白背景上。相反，自然的 场景通常很复杂，并且包含多个实体。图像分割是指分割的过程 视觉场景分为不同的对象和表面，这包括将图形从背景中分割出来(图形- 地面分离)以及将多个对象/表面彼此分割。细分是最基本的 视觉是视觉的功能，是感知、识别和视觉引导行动的门户。然而，神经 分割的基础仍有待理解。一个关键的问题是理解大脑是如何 表示多个视觉刺激，以便可以从 神经元群体的活动。我们在拟议的项目中解决了这个问题，以阐明神经 神经元群体中潜在的分割机制和编码感觉信息的原则。 视觉运动和深度为分割提供了有力的线索。因此，我们的重点是了解 大脑使用运动和深度线索来实现分割。我们在界定如何 中-颞叶(MT)皮质是运动和深度处理的重要区域，代表多个重叠 视觉刺激。我们发现，MT神经元对其中一种成分表现出不同类型的反应偏向 多个刺激，揭示了一套新的规则，通过这些规则，多个刺激在神经元的感受野内相互作用。 这些生理发现与我们关于自然场景统计的初步数据一起导致了我们的假设 视觉系统利用自然场景中的统计规律来区分图形和 并有效地表示多个视觉刺激以实现分割。为了测试这一支配性 假设，我们将整合自然场景统计、神经生理学和理论的方法 对最佳编码的考虑。具体地说，我们将描述深度和运动的自然场景统计数据 与图像分割相关，阐明了立体深度在图形-背景中的作用 分离，定义了MT区神经元代表多个空间分离刺激的规则，这 在自然视觉中常见，并决定了大脑多个区域的信号转换 在背侧视觉通路中实现分割。最后，我们将使用信息最大化方法 以确定多个视觉刺激的神经表示是否对分割是最佳的。这个 拟议的研究严谨地探索多个刺激的相互作用，并有望提供重要的见解 研究视觉系统如何解决自然视觉中具有挑战性的分割问题。

Project Summary/Abstract In natural vision, it is rare to encounter an isolated object presented on a blank background. Instead, natural scenes are often complex and contain multiple entities. Image segmentation refers to the process of partitioning visual scenes into distinct objects and surfaces, which includes segmenting a figure from the background (figure- ground segregation) and segmenting multiple objects/surfaces from each other. Segmentation is a fundamental function of vision and is a gateway to perception, recognition and visually guided action. However, the neural underpinning of segmentation remains to be understood. A key question is to understand how the brain represents multiple visual stimuli such that information regarding individual stimuli can be extracted from the activity of populations of neurons. We address this question in the proposed project to elucidate the neural mechanisms underlying segmentation and the principles of coding sensory information in neuronal populations. Visual motion and depth provide potent cues for segmentation. Therefore we focus on understanding how the brain uses motion and depth cues to achieve segmentation. We have made substantial progress in defining how middle-temporal (MT) cortex, an area important for motion and depth processing, represents multiple overlapping visual stimuli. We found that MT neurons show various types of response biases toward one component of multiple stimuli, revealing a set of novel rules by which multiple stimuli interact within neurons’ receptive fields. These physiological findings together with our preliminary data on natural scene statistics led us to hypothesize that the visual system exploits the statistical regularities in natural scenes that differentiate figure from the background and represents multiple visual stimuli efficiently to achieve segmentation. To test this overarching hypothesis, we will integrate the approaches of natural scene statistics, neurophysiology, and theoretical consideration of optimal coding. Specifically, we will characterize natural scene statistics of depth and motion pertinent to image segmentation, elucidate the functional roles of stereoscopic depth in figure-ground segregation, define the rules by which neurons in area MT represent multiple spatially-separated stimuli, which are commonly encountered in natural vision, and determine the signal transformation across multiple brain areas in the dorsal visual pathway to achieve segmentation. Finally, we will use an Information-Maximization approach to determine whether the neural representation of multiple visual stimuli is optimal for segmentation. The proposed study rigorously explores the interaction of multiple stimuli and is expected to provide important insight into how the visual system solves the challenging problem of segmentation in natural vision.