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Neural Codes Underlying Visual Segmentation

视觉分割背后的神经代码

基本信息

批准号：
10662383
负责人：
Xin Huang
金额：
$ 45万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
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

项目摘要

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。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Transformations of sensory information in the brain reflect a changing definition of optimality.

大脑中感觉信息的转变反映了最优性定义的变化。

DOI：
10.1101/2023.03.24.534044
发表时间：
2023
期刊：
bioRxiv : the preprint server for biology
影响因子：
0
作者：
Manning,TylerS;Alexander,Emma;Cumming,BruceG;DeAngelis,GregoryC;Huang,Xin;Cooper,EmilyA
通讯作者：
Cooper,EmilyA

Transformations of sensory information in the brain suggest changing criteria for optimality.