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: 10006884
• 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/10006884
• 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.

项目摘要 如何在上颞（IT）皮质（大脑）中组织复杂的视觉物体的表示形式 负责对象识别的地区？迄今为止，针对面孔，身体和诸如面孔和 已经找到了场景，但是绝大多数IT皮层都是“野生”，缺乏任何已知的专业化。各种各样的 已经提出了针对包裹的计划，但对IT组织的全面理解仍然存在 难以捉摸。在这里，我们建议使用fMRI，微刺激和电生理学发展统一 了解猕猴的组织和编码原理。实验是 由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动机。首先，冒险 对象分类训练的深网络可以生成一个参数对象 空间，提供一个定量框架来破译单个IT单元的特征选择性。第二，我们的 初步结果表明，很大一部分猕猴的皮质是地形组织的 根据对象空间的前两个主要组件。这个地形至少包含 四个不同的网络，每个网络至少具有三个层次结构的节点的越来越不变性，并包括 先前描述的面部和身体补丁网络。此外，每个网络中的单个单元格都在投影 传入的对象，格式为对象空间中的向量，在特定的首选轴上。总的来说，这些 结果表明了IT组织的一个新假设：IT皮层由网络铺有瓷砖（即 功能连接的节点，其中节点是IT单元格的一个斑点），其组织和编码原理是 与面部补丁网络非常相似，这些网络的布局遵循常规 地形由对象空间的统计结构指定。 我们提出了三个特定的目标，以严格检验这一假设。在AIM 1中，我们将系统地绘制所有 它的网络是单个动物的。在AIM 2中，我们将记录每个已确定网络中的单元格的响应 到一组庞大的常见对象刺激并确定其编码方案。在AIM 3中，我们将在 每个网络并定量评估对对象识别行为的影响。这三个目标共同寻求 建立对IT组织的全面理解，该组织将fMRI，单位单位和行为桥接。 我们的实验室已经开发了强大的实验技术来解决这些目标，并以前应用了 他们进入猕猴的面部贴片系统。我们认为，将这些技术应用于 更大的问题是如何表示所有对象，而不仅仅是面孔。就像Mendeleev的 化学元素根据其原子质量和化学特性的排列有助于阐明 原子的电子结构，我们认为所有网络的系统映射和表征 它将有助于阐明物体识别的基本神经机制。