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.

项目摘要 复杂视觉对象的表征是如何在下颞叶皮层（IT）中组织的 负责物体识别的区域？到目前为止，区域选择性的几个类别，如脸，身体， 虽然已经发现了一些类似的场景，但绝大多数IT皮层是“野生的”，缺乏任何已知的专业化。各种 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 难以捉摸。在这里，我们建议使用fMRI，微刺激和电生理学来开发一个统一的 理解猕猴信息技术的组织和编码原理。实验是 这是由计算机视觉的一项重大进展和我们实验室的两项关键初步成果推动的。第一，降临 经过对象分类训练的深度网络使得生成参数化对象成为可能 空间，提供了一个定量的框架来破译单个IT细胞的特征选择性。二是我们 初步结果表明，猕猴IT皮质的大部分是地形组织的， 根据物空间的前两个主分量，对物体进行空间重构。这种地形至少包括 四个不同的网络，每个网络至少有三个视图不变性不断增加的分层节点，并包括 先前描述的面部和身体贴片网络。此外，每个网络中的单个细胞都在投射 传入对象，在对象空间中格式化为矢量，到特定的首选轴上。综上所述各项 结果提出了IT组织的新假设：IT皮层由网络平铺（即，套 功能上连接的节点，其中节点是IT单元的补丁），其组织和编码原则是 非常类似于人脸贴片网络，这些网络的布局遵循规则 由物方空间的统计结构指定的地形。 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 网络中的个体动物。在目标2中，我们将记录每个已识别网络中细胞的响应 到一个大的，共同的一组对象刺激，并确定其编码方案。在目标3中，我们将扰乱 每个网络和定量评估对对象识别行为的影响。这三个目标旨在 建立一个全面的理解IT组织，桥梁功能磁共振成像，单个单元，和行为。 我们的实验室已经开发出强大的实验技术来解决这些目标，并已应用于 猕猴面部贴片系统。我们认为，将这些技术应用于 更大的问题是如何表现所有的物体--不仅仅是脸。就像门捷列夫 根据原子质量和化学性质排列化学元素有助于阐明 原子的电子结构，我们相信所有网络的系统映射和表征， 它将有助于阐明物体识别的基本神经机制。