DISCOVERING THE RULES FOR THE ORGANIZATION OF MACAQUE INFEROTEMPORAL CORTEX.

项目摘要 复杂视觉对象在大脑次颞叶(IT)皮质中的表征是如何组织的 负责物体识别的区域？到目前为止，针对几个类别的区域，如面、身体和 场景已经被发现，但绝大多数的IT皮层是“狂野的”，缺乏任何已知的专门化。五花八门 已经提出了打包IT的方案，但对IT组织的全面理解仍然存在 难以捉摸。在这里，我们建议使用功能磁共振成像、微刺激和电生理学来开发一种统一的 了解猕猴IT的组织和编码原则。这些实验是 动力来自于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。第一，新时代的到来 为对象分类而训练的深度网络使得有可能生成参数对象 空间，为破译单个IT细胞的功能选择性提供了一个量化框架。第二，我们的 初步结果表明，猕猴的IT皮质有很大一部分是按地形组织的 根据对象空间的前两个主成分确定。这一地形至少包括 四个不同的网络，每个网络至少具有三个递增的视图不变性的分层节点，并包括 前面描述了面部和身体贴片网络。此外，每个网络中的单个小区都在投射 传入对象，格式化为对象空间中的矢量，放到特定的首选轴上。这些加在一起， 结果为IT组织提出了一个新的假设：IT皮质由网络(即 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 非常类似于人脸贴片网络，这些网络的布局遵循规则 由物体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说。在目标1中，我们将系统地映射所有 单个动物的IT网络。在目标2中，我们将记录每个已识别网络中的细胞的响应 并确定它们的编码方案。在目标3中，我们将扰乱 并定量评估每个网络对目标识别行为的影响。这三个目标加在一起，旨在 建立对IT组织的全面了解，将功能磁共振成像、单个单位和行为联系起来。 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，并已在之前应用 他们到猕猴面部贴片系统。我们认为，将这些技术应用于 更大的问题是如何表示所有对象--而不仅仅是面部。就像门捷列夫 化学元素根据原子质量和化学性质的排列有助于阐明 原子的电子结构，我们相信所有网络的系统映射和表征 这将有助于阐明物体识别的基本神经机制。

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.