EFFICIENT CODING IN VISUAL CORTEX

视觉皮层的高效编码

• 批准号: 6530877
• 负责人: Bruno A. OLSHAUSEN
• 金额: $ 9.96万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-03-01 至 2004-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6530877
• 关键词: behavior prediction; behavioral /social science research tag; clinical research; computational neuroscience; discrimination learning; form /pattern perception; human subject; model design /development; neural information processing; psychological tests; psychophysics; visual cortex; visual feedback; visual perception

DESCRIPTION (Adapted from applicant's abstract): A major limitation to our understanding of visual cortical function is the lack of computational theories capable of making useful, testable predictions for what the cortex should be doing. The purpose of this study is to investigate what may be learned about information processing in visual cortex from efficient coding principles. Methods will be developed for representing the structure in images based on probabilistic inference, and these will be related to known neurobiological substrates in a detailed manner in order to make predictions about visual cortical function. Understanding how the cortex processes visual information is an important step in developing therapies for patients who have lost aspects of visual function due to cortical damage, as well as in the development of visual prostheses capable of providing appropriate cortical stimulation from artificial vision devices. The aspects of visual cortical function that the study aims to shed light on are the properties of feature selectivity, form-invariance, and the role of feedback connections in shaping neural response properties and in mediating visual perception. These issues will be addressed as part of five specific aims. The first is to develop a functional model of the horizontal connections in area V1 based on the statistical structure of natural images. This model will be related to the structure of long-range horizontal fibers in order to make predictions about the role of this form of feedback within V1. The second aim is to develop a model neural system capable of learning the structure of objects independent of variations in position, size, or other geometric transformations. The model will be used to help understand how form-invariance is established in cortical neurons. The third aim is to formulate a model of occlusion in images, which will be used to shed light on how figure-ground segregation could be performed by cortical mechanisms. The fourth aim is to develop a functional model of top-down cortical feedback based on a hierarchical image model. The existence of such a system that utilizes top-down feedback to solve practical problems in vision will help to elucidate a possible role for two-way information processing in the cortical hierarchy. The fifth aim is to test these models through psychophysical experiments. The results of these studies will lead to advances in our understanding of information processing in visual cortex, and possibly shed light on the nature of cortical information processing in general.

描述（改编自申请人的摘要）：我们的一个主要限制 对视觉皮层功能的理解缺乏计算能力 能够对大脑皮层的作用做出有用的、可测试的预测的理论 应该做的。 这项研究的目的是调查什么可能是 从高效编码中了解视觉皮层的信息处理 原则。 将开发用于表示结构的方法 基于概率推理的图像，这些图像将与已知的相关 详细地了解神经生物学底物以便做出预测 关于视觉皮层功能。 了解皮层如何处理 视觉信息是为患者开发治疗方法的重要一步 由于皮质损伤而丧失部分视觉功能的人，以及 开发能够提供适当的视觉假体 来自人工视觉设备的皮质刺激。 该研究旨在阐明视觉皮层功能的各个方面 是特征选择性、形式不变性和作用的属性 反馈连接在塑造神经反应特性和调节中的作用 视觉感知。 这些问题将作为五个具体问题的一部分得到解决 目标。 首先是开发水平功能模型 基于自然图像统计结构的区域 V1 中的连接。 该模型将与长程水平纤维的结构有关 为了预测这种形式的反馈在内部的作用 V1。 第二个目标是开发一个能够学习的模型神经系统 物体的结构与位置、大小或变化无关 其他几何变换。 该模型将用于帮助理解 如何在皮质神经元中建立形式不变性。 第三个目标是 制定图像中的遮挡模型，该模型将用于阐明 关于如何通过皮质机制进行图形-背景分离。 第四个目标是开发自上而下的皮质功能模型 基于分层图像模型的反馈。 存在这样一个 利用自上而下的反馈来解决视觉中的实际问题的系统 将有助于阐明双向信息处理的可能作用 皮质层次结构。 第五个目标是通过测试这些模型 心理物理实验。 这些研究的结果将导致 我们对视觉皮层信息处理的理解取得了进展， 并可能揭示皮层信息处理的本质 一般的。

项目成果

A graphical anatomical database of neural connectivity.

神经连接的图形解剖数据库。

• DOI: 10.1098/rstb.2001.0907
• 发表时间: 2001
• 期刊: Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
• 作者: Press,WA;Olshausen,BA;VanEssen,DC
• 通讯作者: VanEssen,DC