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/摘要在自然的视野中，很少会遇到在空白背景上呈现的孤立物体场景通常是复杂的，包含多芯片实体。视觉场景变成不同的对象和表面，其中包括从背景中分割一个图（图 - 地面分割）和分割的对象/表面彼此分割。视力的功能，是通往感知的门户，但是神经，神经。分割的基础尚待理解。代表多种视觉刺激，以便可以从您中提取有关单个刺激的信息神经元种群的活动。分段的基础机制和神经元种群中的感觉信息的原理。视觉运动和深度为分割提供有效的提示。大脑美国运动和深度提示以实现分割。中颞（MT）皮质是运动和深度处理重要的区域，代表多个重叠视觉刺激。我们发现MT神经元显示出各种反应偏见多种刺激，陶醉于一组新规则，通过这些规则，多个刺激在神经元的接受场中相互作用。 Thysiolological发现以及我们关于自然场景统计数据的预预启示数据导致我们假设视觉系统利用自然尺度的统计规律性，使图与您区分开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最佳编码的考虑。与图像分割有关，阐明了立体深度在图 - 地面中的功能作用隔离，定义区域中神经元MT代表多个空间分离的刺激的规则，通常在自然视觉中遇到，并确定多个大脑区域的信号转换在背面视觉途径中以实现分割。确定神经是否代表多个视觉刺激是最佳的支撑研究严格探讨了多个刺激的相互作用，并有望提供重要的见解视觉系统如何解决自然视觉中分割的挑战问题。

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.