Visual pattern representation in extrastriate cortex

纹外皮层的视觉模式表征

• 批准号: 10018018
• 负责人: J ANTHONY MOVSHON
• 金额: $ 38.74万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10018018
• 关键词: Affect; Agnosia; Area; Behavioral; Blindness; Brain Injuries; Cells; Cerebral cortex; Complement; Complex; Data; Development; Dimensions; Disease; Electrophysiology (science); Elements; Event; Foundations; Future; Goals; Human; Image; Lateral Geniculate Body; Location; Macaca; Measurement; Measures; Memory; Methodology; Methods; Modeling; Motion; Motivation; Nervous system structure; Neurons; Noise; Outcome; Pattern; Performance; Play; Population; Prevalence; Primates; Property; Psychophysics; Recovery; Research; Retina; Role; Sensory; Shapes; Signal Transduction; Space Models; Statistical Models; Stimulus; Structural Models; Structure; Testing; Texture; V1 neuron; V2 neuron; V4 neuron; Vision Disorders; Visual; Visual Cortex; Visual Fields; Work; area MT; area V1; area V2; area V4; area striata; awake; design; disability; experimental study; extrastriate visual cortex; human subject; individual response; interest; neural model; neuroimaging; object shape; operation; preservation; receptive field; response; statistics; success; tool; visual information

Project Summary / Abstract The goal of this research is to discover the cortical computations that determine the response properties of neurons in visual cortical areas V2 and V4, two of the largest visual areas outside the primary visual cortex in primates. V2 receives a strong direct input from V1, and depends on the functional integrity of V1 for its visual responsiveness. We will therefore develop a two-stage model, in which responses are constructed from a suitable combination of V1 afferents, with the design of each stage following a common canonical form. This model is designed to account for the visual response properties of neurons as economically as possible, allowing it to be fit to data recorded from single neurons. The model will also be able to generate population representations that can predict perceptual capabilities. V4, in turn, receives the bulk of its direct input from V2. We will measure V4 responses with the goal of building a similar model of the way it transforms input from V2. Some neurons in V4 respond selectively to images of objects and shapes while others seem to be more sensitive to local image statistics. The motivation for the structure of the V2 model, and our confidence in its success, comes from the convergence of three pieces of previous work: (1) we have developed, fit, and validated a similar two-stage model for neuronal responses in area MT, an extrastriate area which also receives primary afferent drive from area V1; (2) we have preliminary evidence for a key model element, which is to represent the operation of V2 neurons as derivatives over the space of its inputs, and (3) we have shown that models of this structure can account for our previous discovery that many V2 cells respond more vigorously to naturalistic texture stimuli than to matched noise. We will complement the modeling by analyzing the local structure of natural images and the psychophysical performance of human observers in the space that the model is designed to capture. We are not yet ready to build a principled model of how V4 combines inputs from V2. We believe that V4's functional circuitry will be similar to that of V2, but in advance of the model we will build for V2 we lack a sufficiently precise account of V4's inputs. But we have evidence from previous studies of V4 and from human neuroimaging data that a key component of V4's response is its selectivity for complex forms, so we will make measurements of V4 responses along a key image continuum between natural form and image statistics, and will also measure the role of contextual signals in establishing V4's selectivity. The outcome of this work will be a new understanding of the functions of V2 and V4, which plays a pivotal role in the elaboration of visual information in the cerebral cortex.

项目总结/摘要 这项研究的目标是发现大脑皮层的计算决定了大脑皮层的反应特性。 视觉皮层V2和V4区的神经元，这两个区域是初级视觉皮层外最大的视觉区域， 灵长类动物V2接收来自V1的强大的直接输入，并且依赖于V1的功能完整性来实现其视觉效果。 响应能力。因此，我们将开发一个两阶段模型，其中响应是从一个 V1传入的适当组合，每个阶段的设计遵循一个共同的标准形式。这 模型被设计为尽可能经济地考虑神经元的视觉响应特性， 允许它适合于从单个神经元记录的数据。该模型还将能够生成人口 可以预测感知能力的表征。V4又从V2接收其直接输入的大部分。 我们将测量V4响应，目的是建立一个类似的模型，它转换来自V2的输入。 V4中的一些神经元对物体和形状的图像有选择性地做出反应，而其他神经元似乎对物体和形状的图像更敏感。 对本地图像统计敏感。 V2模型结构的动机以及我们对其成功的信心来自于 收敛的三件以前的工作：（1）我们已经开发，适合，并验证了类似的两阶段 MT区的神经元反应模型，MT区是一个纹状体外区域，也接受来自 区域V1;（2）我们有一个关键的模型元素的初步证据，这是代表V2的操作 神经元作为其输入空间的导数，（3）我们已经证明，这种结构的模型可以 这也解释了我们先前的发现，即许多V2细胞对自然纹理刺激的反应更强烈 而不是匹配的噪声。我们将通过分析自然图像的局部结构来补充建模 以及人类观察者在该模型旨在捕捉的空间中的心理物理表现。 我们还没有准备好建立一个V4如何结合V2输入的原则模型。我们相信V4 功能电路将类似于V2的功能电路，但在我们为V2构建模型之前，我们缺乏 对V4输入的足够精确的描述。但我们有证据表明，从以前的研究V4和人类 神经影像学数据表明，V4反应的一个关键组成部分是它对复杂形式的选择性，所以我们将 沿着自然形态和图像统计之间的关键图像连续体沿着测量V4响应，以及 还将测量上下文信号在建立V4选择性中的作用。 这项工作的结果将是对V2和V4功能的新认识，V2和V4在其中起着关键作用。 大脑皮层对视觉信息的加工。