Neural Computations in Visual Cortex

视觉皮层的神经计算

• 批准号: 7992887
• 负责人: Jonathan D Victor
• 金额: $ 47.61万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7992887
• 关键词: Accounting; Alzheimer' s Disease; Amblyopia; Architecture; Autistic Disorder; Behavior; Brain; Characteristics; Chronic Brain Injury; Complex; Developmental Process; Diagnosis; Entropy; Feedback; Funding; Goals; Image; Individual; Laboratories; Macaca; Measures; Modeling; Morbidity - disease rate; Nature; Neurons; Noise; Pathway interactions; Pattern; Perception; Perceptual disturbance; Play; Population; Process; Recurrence; Research; Role; Shapes; Stimulus; Stroke; Structure; Testing; Texture; V1 neuron; Vision; Visual; Visual Cortex; Visual Perception; Work; area striata; base; cell assembly; cognitive function; design; insight; luminance; meetings; millimeter; network models; public health relevance; relating to nervous system; response; spatiotemporal; statistics; success; tool; visual information

DESCRIPTION (provided by applicant): The goal of this research is to understand the nature of the computations performed by primary visual cortex (V1), and how these calculations are carried out. Even the most basic step in interpreting the visual world -- extracting local features such as lines and edges -- is a difficult computational problem: it must be carried out in the context of cluttered, complex, natural visual scenes; it must be carried out rapidly; and it must be carried out by neural hardware. The generally accepted view is that V1 acts primarily as a feedforward bank of filters, in which feedback and gain controls play a modulatory role. However, models constructed from simple analytically-convenient stimuli provide an incomplete account of responses to natural scenes. Since natural scenes have characteristics that traditional analytic stimuli lack, this observation implies that V1 neurons are sensitive to these distinguishing characteristics, namely, high-order statistics (HOS's). Based on several lines of evidence (including work from the previous funding period and studies in other laboratories), we hypothesize that this sensitivity to HOS's indicates that V1's basic design is that of a strongly recurrent network. In particular, we hypothesize that the characteristics that distinguish a strongly recurrent architecture from a feedforward or modulatory feedback architecture account for V1's ability to extract HOS's. To test these hypotheses, we focus on analyzing V1's responses to stimuli containing HOS's -- because they distinguish among these two contrasting pictures of V1, and because HOS's are precisely the statistical feature that distinguishes natural scenes from traditional analytic stimuli. In Aim 1, we determine the extent of sensitivity of V1 neurons to HOS's, explicitly studying both artificially- constructed stimuli and stimuli derived from natural scenes. In Aim 2, we determine whether dynamic formation of neural assemblies underlies the extraction of HOS's, by analyzing the statistics of multineuronal firing patterns. If successful, this work will provide fundamental insights into the design principles of V1, including how it exploits general features of cortical architecture to carry out the calculations necessary for vision, how sparse representations arise, and the functional significance of cortical neural "noise." PUBLIC HEALTH RELEVANCE: The long-term goal of this project is to understand how the brain analyzes incoming visual information. An enhanced understanding of this process will advance our ability to diagnose and remediate disturbances of perception and cognitive function, which cause significant morbidity in conditions as disparate as amblyopia, autism, Alzheimer's Disease, stroke, and chronic brain injury.

描述（由申请人提供）：本研究的目标是了解初级视觉皮层（V1）执行计算的性质，以及如何执行这些计算。即使是解释视觉世界的最基本步骤-提取局部特征，如线条和边缘-也是一个困难的计算问题：它必须在混乱，复杂，自然的视觉场景中进行;它必须快速进行;它必须由神经硬件执行。普遍接受的观点是，V1主要充当滤波器的前馈组，其中反馈和增益控制起调节作用。然而，从简单的分析方便的刺激构建的模型提供了一个不完整的帐户的自然场景的反应。由于自然场景具有传统分析刺激所缺乏的特征，因此这一观察结果意味着V1神经元对这些区别特征，即高阶统计量（HOS）敏感。基于几条证据线（包括上一个资助期的工作和其他实验室的研究），我们假设这种对HOS的敏感性表明V1的基本设计是一个强循环网络。特别是，我们假设的特点，区分一个强烈的经常性的架构，从前馈或调制反馈架构占V1的能力，提取HOS的。为了检验这些假设，我们集中分析V1的反应刺激包含HOS的-因为他们区分这两个对比图片V1，因为HOS的正是统计特征，区分自然场景从传统的分析刺激。在目标1中，我们确定V1神经元对HOS的敏感程度，明确地研究了人工构造的刺激和来自自然场景的刺激。在目标2中，我们确定是否动态形成的神经组件的基础上提取的HOS的，通过分析多神经元放电模式的统计。如果成功，这项工作将为V1的设计原理提供基本的见解，包括它如何利用皮层结构的一般特征来进行视觉所需的计算，稀疏表示如何产生，以及皮层神经“噪音”的功能意义。" 公共卫生相关性：该项目的长期目标是了解大脑如何分析传入的视觉信息。对这一过程的深入了解将提高我们诊断和补救感知和认知功能障碍的能力，这些障碍会导致弱视、自闭症、阿尔茨海默病、中风和慢性脑损伤等不同疾病的严重发病率。