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.

项目总结