Understanding the circuit for topological object tracking

了解拓扑对象跟踪电路

基本信息

批准号：
8352029
负责人：
Doris Ying Tsao
金额：
$ 82万
依托单位：
CALIFORNIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-30 至 2017-07-31
项目状态：
已结题

项目摘要

DESCRIPTION Abstract: Understanding the circuit for topological object tracking (Science Area: Neuroscience) The problem I want to solve is how an object first arises in the brain. Light hitting the retina is caried by over a million axons of the optic nerve into primary visual cortex. These are the pixels that drive visual experience. But when we look around us, we don't see pixels. We see invariant objects in space--invariant in that we perceive the objects as unchanged despite severe changes in appearance as we move around them. How does the brain stitch together pixels into invariant, discrete objects in space? The time is ripe for a fresh attack on this problem due to a critical theoretical advance, and a host of experimental advances. I believe the essential reason why no one has solved the problem of invariant object perception until now is that no one has realized the answer could be very simple. A new mathematical theory explains how the representation of objects in the 3D visual world as surfaces enables a complete and fundamentally simple solution to the problem of object segmentation and tracking, i.e., labeling all the pixels belonging to a single object over space and time, regardless of object shape. The theory strongly suggests that a powerful ""topological engine"" is churning away within very early stages of the visual cortex, to generate invariant labels for the different objects in the environment over space and time, and specifies the computations that must be performed in order to generate these invariant labels. Motivated by this new theory, and taking advantage of several key recent experimental advances in monkey and rodent vision research, I describe a set of experiments to: 1) identify the neural signature of the topological object label in early macaque visual cortical areas, 2) behaviorally test whether rodents also generate a surface representation, and if so, then 3) dissect the circuit by which this label is generated through two photon imaging an

描述抽象的：理解拓扑对象跟踪的电路（科学领域：神经科学）我想解决的问题是对象如何在大脑中首先出现。照射到视网膜的光线被超过一百万个视神经轴突传送到初级视觉皮层。这些是驱动视觉体验的像素。但当我们环顾四周时，我们看不到像素。我们在空间中看到不变的物体——不变是因为当我们在物体周围移动时，尽管物体的外观发生了严重的变化，但我们仍然认为物体没有变化。大脑如何将像素拼接成空间中不变的、离散的物体？由于关键的理论进展和大量实验进展，对这个问题进行新的研究的时机已经成熟。我相信迄今为止没有人解决不变物体感知问题的根本原因是没有人意识到答案可能非常简单。一种新的数学理论解释了如何将 3D 视觉世界中的对象表示为表面，从而为对象分割和跟踪问题提供完整且从根本上简单的解决方案，即在空间和时间上标记属于单个对象的所有像素，无论的物体形状。该理论强烈表明，一个强大的“拓扑引擎”正在视觉皮层的早期阶段运转，为环境中的不同物体在空间和时间上生成不变的标签，并指定必须在为了生成这些不变的标签。受这一新理论的推动，并利用猴子和啮齿动物视觉研究方面的几个关键的最新实验进展，我描述了一组实验：1）识别早期猕猴视觉皮层区域拓扑对象标签的神经特征，2）行为测试啮齿类动物是否也产生表面表征，如果是，则 3) 解剖通过两个光子成像生成该标签的电路