Linking Neural Population Activity and Visual Perception

将神经群体活动与视觉感知联系起来

• 批准号: 9887458
• 负责人: EYAL J SEIDEMANN
• 金额: $ 38.6万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9887458
• 关键词: Address; Animals; Area; Behavior; Behavioral; Biological; Clutterings; Complex; Detection; Discrimination; Family; Foundations; Future; Genetic Techniques; Goals; Heart; Image; Lead; Learning; Life; Link; Macaca; Masks; Mathematics; Measures; Mediating; Modeling; Monitor; Monkeys; Neurons; Optics; Pattern; Play; Population; Primates; Problem Solving; Property; Research; Role; Signal Transduction; Stimulus; Techniques; Testing; Texture; Visual; Visual Cortex; Visual Perception; Visual system structure; area striata; automated segmentation; base; calcium indicator; design; experimental study; multi-scale modeling; neuromechanism; neurophysiology; novel; optogenetics; predicting response; relating to nervous system; response; retinotopic; segregation; spatiotemporal; two-photon; voltage sensitive dye

Project Summary The overarching goal of the current proposal is to uncover the neural mechanisms that underlie figure-ground segregation, an important component of natural scene interpretation, focusing on the role of the primary visual cortex (V1) and its dynamic interactions with higher visual cortical areas. Specifically, we will measure how V1 responds to a wide range of backgrounds, including artificial and naturalistic textures, with and without occluding targets, and develop computational encoding models that would allow one to predict V1 population responses at multiple biologically-relevant spatial scales to arbitrary figures and backgrounds (Aim 1). In Aim 2 we will study the role of V1 in figure-ground discrimination by measuring and perturbing V1 population responses while animals detect a camouflaged target that occludes a background of the same texture family. These experiments will be used to distinguish between different candidate decoding models that describe how single-trial V1 population responses lead to behavior in the task. An important goal would be to test whether V1 plays an active role in the segmentation computation. The alternative is that V1 provides the feedforward input to a segmentation computation that occurs downstream, and receives top-down figure-ground signals only after the segmentation had been completed. Overall, the proposed experiments will lead to a deeper understanding of the role of V1 in fundamental mid-level visual computations and will serve as an important step toward a quantitative understanding of the representation of complex natural scenes by populations of neurons in V1.

项目摘要 目前这项研究的首要目标是揭示图形背景背后的神经机制 隔离，自然场景解释的重要组成部分，侧重于初级视觉的作用 皮层（V1）及其与高级视觉皮层区域的动态相互作用。具体来说，我们将测量V1 响应范围广泛的背景，包括人工和自然的纹理，有和没有 封闭目标，并开发计算编码模型，使人们能够预测V1种群 在多个生物相关的空间尺度上对任意图形和背景的响应（目标1）。在目标2中 我们将通过测量和扰动V1的总体来研究V1在图形-背景辨别中的作用 当动物检测到遮挡相同纹理家族背景的隐藏目标时， 这些实验将用于区分不同的候选解码模型，这些模型描述了如何 单次试验V1群体反应导致任务中的行为。一个重要的目标是测试V1是否 在分割计算中起着积极的作用。替代方案是V1提供前馈输入 到发生在下游的分段计算，并且仅接收自上而下的图形-背景信号 在分割完成之后。总的来说，拟议的实验将导致更深层次的 理解V1在基础中级视觉计算中的作用，并将作为一个重要的 一步走向量化的理解复杂的自然场景的人口的代表性， V1神经元。