Understanding the circuit for topological object tracking

了解拓扑对象跟踪电路

• 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
• 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
• 来源: https://reporter.nih.gov/project-details/8352029
• 关键词: Appearance; Area; Axon; Brain; Environment; Image; Label; Light; Macaca; Monkeys; Neurosciences; Optic Nerve; Problem Solving; Retina; Rodent; Science; Solutions; Specific qualifier value; Staging; Surface; Testing; Time; Vision research; Visual; Visual Cortex; abstracting; area striata; experience; mathematical theory; object perception; object shape; relating to nervous system; research study; theories; two-photon

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 摘要： 了解拓扑对象跟踪的电路（科学领域：神经科学）我想解决的问题是一个对象是如何在大脑中首先出现的。光照射到视网膜上，由超过一百万个视神经轴突传递到初级视皮层。这些是驱动视觉体验的像素。但是当我们环顾四周时，我们看不到像素。我们在空间中看到不变的物体--不变的是，尽管我们在周围移动时物体的外观发生了严重的变化，但我们认为物体是不变的。大脑如何将像素拼接成空间中不变的离散对象？由于一个关键的理论进展和大量的实验进展，对这个问题进行新的攻击的时机已经成熟。我认为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以非常简单。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，从而实现对象分割和跟踪问题的完整且基本简单的解决方案，即，在空间和时间上标记属于单个对象的所有像素，而不管对象形状如何。该理论强烈地表明，在视觉皮层的早期阶段，一个强大的“拓扑引擎”正在搅动，为环境中的不同物体在空间和时间上生成不变的标签，并指定必须执行的计算，以生成这些不变的标签。受这一新理论的启发，并利用最近在猴子和啮齿动物视觉研究中的几个关键实验进展，我描述了一组实验：1）识别早期猕猴视觉皮层区域中拓扑对象标签的神经签名，2）行为测试啮齿动物是否也产生表面表征，如果是，然后3）解剖通过双光子成像产生该标记的电路，