Neural Codes Underlying Visual Segmentation

视觉分割背后的神经代码

• 批准号: 10261445
• 负责人: Xin Huang
• 金额: $ 43.65万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10261445
• 关键词: Address; Area; Brain; Cerebral cortex; Code; Complex; Cues; Data; Databases; Dorsal; Dyslexia; Experimental Designs; Feedback; Funding; Goals; Impairment; Individual; Investigation; Light; Link; Location; Measures; Monkeys; Motion; Neural Network Simulation; Neurons; Noise; Patients; Pattern; Perception; Physiological; Population; Process; Property; Research; Role; Sensory; Signal Transduction; Speed; Stimulus; Stream; Surface; Systematic Bias; Temporal Lobe; Testing; Vision; Vision Disparity; Visual; Visual Agnosias; Visual Motion; Visual Pathways; Visual system structure; Work; area MT; area V1; imaging Segmentation; insight; nervous system disorder; neuromechanism; neurophysiology; novel; receptive field; relating to nervous system; response; segregation; sensor technology; statistics; stereoscopic; visual stimulus

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.

项目概要/摘要 在自然视觉中，很少会遇到呈现在空白背景上的孤立物体，而是自然的。 场景通常很复杂并且包含多个实体图像分割是指分区的过程。 视觉场景分解为不同的物体和表面，其中包括从背景中分割图形（图- 地面隔离）并将多个物体/表面彼此分割。 视觉的功能，是感知、识别和视觉引导行动的门户。 分割的基础仍有待理解，一个关键问题是了解大脑是如何工作的。 代表多个视觉刺激，以便可以从其中提取有关单个刺激的信息 我们在拟议的项目中解决了这个问题，以阐明神经元的活动。 分割的机制和神经群体中编码感觉信息的原理。 视觉运动和深度为分割提供了有力的线索，因此我们专注于理解如何进行分割。 大脑使用运动和深度线索来实现分割，我们在定义如何进行分割方面取得了实质性进展。 中颞叶 (MT) 皮层是运动和深度处理的重要区域，代表多重重叠 我们发现 MT 神经元对视觉刺激的某一组成部分表现出各种类型的反应偏差。 多重刺激，揭示了多种刺激在神经元感受野内相互作用的一组新规则。 这些生理学发现与我们关于自然场景统计的初步数据一起使我们捕获 视觉系统利用自然场景中的统计规律来区分人物和物体 背景并有效地表示多个视觉刺激以实现分割。 假设，我们将整合自然场景统计、神经生理学和理论的方法 具体来说，我们将描述自然场景的深度和运动统计特征。 与图像分割相关，阐明立体深度在图形-背景中的功能作用 分离，定义 MT 区域中的神经元代表多个空间分离的刺激的规则，其中 在自然视觉中常见，并确定跨多个大脑区域的信号转换 最后，我们将使用信息最大化方法。 确定多个视觉刺激的神经表示是否最适合分割。 拟议的研究严格探索了多种刺激的相互作用，预计将提供重要的见解 研究视觉系统如何解决自然视觉分割的挑战性问题。