Discovering the rules for the organization of macaque inferotemporal cortex.

发现猕猴颞下皮层的组织规则。

• 批准号: 9803682
• 负责人: Doris Ying Tsao
• 金额: $ 57.58万
• 依托单位: CALIFORNIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9803682
• 关键词: Alzheimer' s Disease; Anatomy; Animals; Area; Basic Science; Behavior; Brain; Brain region; Categories; Cells; Chemicals; Classification; Code; Complex; Computer Vision Systems; Diagnosis; Disease; Electric Stimulation; Electrodes; Electrophysiology (science); Elements; Epilepsy; Face; Functional Magnetic Resonance Imaging; Glean; Image; Individual; Macaca; Maps; Measures; Monkeys; Performance; Population; Process; Resolution; Sampling; Scheme; Signal Transduction; Site; Specific qualifier value; Stimulus; Structure; System; Task Performances; Techniques; Temporal Lobe; Testing; Three-Dimensional Image; Time; Training; Visual; chemical property; electronic structure; experimental study; inferotemporal cortex; insight; microstimulation; movie; nervous system disorder; neuromechanism; object perception; object recognition; relating to nervous system; response; vector; visual information

Project Summary How is the representation of complex visual objects organized in inferotemporal (IT) cortex, the large brain region responsible for object recognition? To date, areas selective for a few categories such as faces, bodies, and scenes have been found, but the vast majority of IT cortex is “wild,” lacking any known specialization. Various schemes have been proposed for parceling IT, but a comprehensive understanding of IT organization remains elusive. Here, we propose to use fMRI, microstimulation, and electrophysiology to develop a unified understanding of the organization and coding principles of macaque IT. The experiments are motivated by a major advance in computer vision and two key preliminary results from our lab. First, the advent of deep networks trained for object classification makes it possible to generate a parametric object space, providing a quantitative framework to decipher the feature selectivity of single IT cells. Second, our preliminary results suggest that a large portion of macaque IT cortex is topographically organized according to the first two principal components of object space. This topography encompasses at least four distinct networks, each with at least three hierarchical nodes of increasing view invariance, and includes the previously described face and body patch networks. Furthermore, single cells within each network are projecting incoming objects, formatted as vectors in the object space, onto specific preferred axes. Taken together, these results suggest a new hypothesis for IT organization: IT cortex is tiled by networks (i.e., sets of functionally connected nodes, where a node is a patch of IT cells) whose organization and coding principles are very similar to that of the face patch network, and the layout of these networks follows a regular topography specified by the statistical structure of object space. We propose three Specific Aims to rigorously test this hypothesis. In Aim 1, we will systematically map all networks within IT of individual animals. In Aim 2, we will record responses of cells in each identified network to a large, common set of object stimuli and determine their coding scheme. In Aim 3, we will perturb activity in each network and quantitatively assess effect on object recognition behavior. Together, these three Aims seek to build a comprehensive understanding of IT organization that bridges fMRI, single units, and behavior. Our lab has developed powerful experimental techniques to tackle each of these Aims and has previously applied them to the macaque face patch system. We believe the time is ripe to apply these techniques to the larger problem of how all objects are represented--not just faces. In the same way that Mendeleev’s arrangement of chemical elements according to their atomic mass and chemical properties helped elucidate the electronic structure of atoms, we believe systematic mapping and characterization of all networks in IT will help elucidate the fundamental neural mechanism for object recognition.

项目概要 复杂视觉对象的表征是如何在下颞叶 (IT) 皮层（即大脑）中组织起来的 负责物体识别的区域？迄今为止，区域选择性地针对一些类别，例如面部、身体和 场景已经被发现，但绝大多数 IT 皮层是“野生的”，缺乏任何已知的专业化。 已经提出了对 IT 进行打包的方案，但对 IT 组织的全面理解仍然存在 在这里，我们建议使用功能磁共振成像、微刺激和电生理学来开发一个统一的方法。 了解猕猴IT的组织和编码原理。 受到计算机视觉的重大进步和我们实验室的两个关键初步结果的推动。 用于对象分类的深度训练的网络使得生成参数化对象成为可能 空间，提供了一个定量框架来破译单个 IT 单元的特征选择性。 初步结果表明，猕猴 IT 皮层的很大一部分是按地形组织的 根据物体空间的前两个主要组成部分，该拓扑至少包含。 四个不同的网络，每个网络至少具有三个增加视图不变性的分层节点，并包括 先前描述的面部和身体斑块网络此外，每个网络内的单个细胞正在投射。 传入的对象（在对象空间中格式化为向量）放在特定的首选轴上。 结果提出了 IT 组织的一个新假设：IT 皮层由网络（即一组 功能连接的节点，其中节点是 IT 单元的补丁），其组织和编码原则为 与人脸补丁网络非常相似，并且这些网络的布局遵循规则 由对象空间的统计结构指定的拓扑。 我们提出了三个具体目标来严格检验这一假设，在目标 1 中，我们将系统地绘制所有目标。 在目标 2 中，我们将记录每个已识别网络中细胞的反应。 在目标 3 中，我们将干扰一组大的、常见的对象刺激并确定它们的编码方案。 这三个目标共同寻求每个网络并定量评估对对象识别行为的影响。 建立对 IT 组织的全面了解，将 fMRI、单个单元和行为联系起来。 我们的实验室开发了强大的实验技术来解决这些目标，并且之前已经应用过 我们相信将这些技术应用于猕猴面部贴片系统的时机已经成熟。 更大的问题是如何表示所有物体——而不仅仅是像门捷列夫那样表示人脸。 根据化学元素的原子质量和化学性质进行排列有助于阐明 原子的电子结构，我们相信所有网络的系统映射和表征 信息技术将有助于阐明物体识别的基本神经机制。