Hierarchical cortical circuits implementing robust 3D visual perception

分层皮质电路实现强大的 3D 视觉感知

• 批准号: 10237226
• 负责人: Ari Rosenberg
• 金额: $ 40.76万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10237226
• 关键词: 3-Dimensional; 3D world; Animals; Area; Behavioral; Brain; Brain region; Clutterings; Code; Complex; Cues; Data; Discrimination; Disease; Electrophysiology (science); Environment; Etiology; Eye; Face; Feedback; Frequencies; Future; Goals; Human; Image; Impaired cognition; Industry; Joints; Knowledge; Macaca; Magnetic Resonance Imaging; Measures; Monkeys; Motor output; Neurons; Pathway interactions; Perception; Positioning Attribute; Process; Reliability of Results; Research; Retina; Robot; Sensory; Signal Transduction; Stimulus; Structure; Testing; Uncertainty; Variant; Vision; Vision Disparity; Visual; Visual Perception; Visual system structure; Weight; Work; base; experimental study; falls; imaging approach; improved; insight; monocular; movie; neural circuit; neural correlate; neuroimaging; neurophysiology; orientation selectivity; public health relevance; receptive field; relating to nervous system; response; retinal imaging; retinotopic; sample fixation; stereoscopic; theories; three dimensional structure; two-dimensional; virtual reality; visual information

PROJECT SUMMARY/ABSTRACT How do we perceive the three-dimensional (3D) structure of the world when our eyes only sense two-dimensional (2D) projections like a movie on a screen? Reconstructing 3D scene information from 2D retinal images is a highly complex problem, made evident by the great difficulty robots have in turning visual inputs into appropriate 3D motor outputs to move physical chessmen on a cluttered board, even though they can beat the best human chess players. The goal of this proposal is to elucidate how hierarchical cortical circuits implement robust (i.e., accurate & precise) 3D visual perception. Towards this end, we will answer two fundamental questions about how the brain achieves the 2D-to-3D visual transformation using behavioral, electrophysiological, and neuro- imaging approaches. In Aim 1, we will answer the question of how the visual system represents the spatial pose (i.e., position & orientation) of objects in 3D space. Our hypothesis is that 3D scene information is reconstructed within the V1  V3A  CIP pathway. We will test this hypothesis by simultaneously recording 3D pose tuning curves from V3A and CIP neurons in macaque monkeys while the animals perform an eight-alternative 3D orientation discrimination task. This experiment will dissociate neural responses to 3D pose that reflect elementary receptive field structures (resulting in 3D orientation preferences that vary with position-in-depth, which we anticipate to find in V3A) from those that represent 3D object features (resulting in 3D orientation preferences that are invariant to position-in-depth, which we anticipate to find in CIP). Using these data, we will additionally test for functional correlates between neural activity in each area and perceptual sensitivity. Through application of Granger Causality Analysis to simultaneous local field potential recordings in V3A and CIP, we will further test for feedforward/feedback influences between the areas to evaluate their hierarchical structure. In Aim 2, we will answer the question of how binocular disparity cues (differences in where an object's image falls on each retina) and perspective cues (features resulting from 2D retinal projections of the 3D world) are integrated at the perceptual and neuronal levels to achieve robust 3D visual representations. Both cues provide valuable 3D scene information, and human perceptual studies show that their integration is dynamically reweighted depending on the viewing conditions (i.e., position-in-depth & orientation-in-depth) to achieve robust 3D percepts. Specifically, greater weight is assigned to the more reliable cue based on the viewing conditions; but, where and how this sophisticated integrative process is implemented in the brain is unknown. We anticipate that V3A and CIP will each show sensitivity to both cue types, but only CIP will dynamically reweight the cues to achieve robust 3D representations. This research is important for understanding ecologically relevant sensory processing and neural computations that are required for us to successfully interact with our 3D environment. Insights from this work will also extend beyond 3D vision by elucidating processes implemented by neural circuits to solve highly nonlinear optimization problems that turn ambiguous sensory signals into robust perceptions.

项目摘要/摘要 当我们的眼睛只感知二维时，我们如何感知世界的三维（3D）结构 （2D）像屏幕上的电影一样的投影？从2D视网膜图像重建3D场景信息是一个 高度复杂的问题，由巨大的机器人在将视觉输入变成适当的情况下提供的证据 3D电动机输出以在混乱的板上移动物理棋手，即使他们可以击败最好的人 国际象棋球员。该提案的目的是阐明层级皮层电路如何实施强大的（即 精确且精确）3D视觉感知。为此，我们将回答两个有关 大脑如何使用行为，电生理和神经 - 成像方法。在AIM 1中，我们将回答视觉系统如何代表空间姿势的问题 （即位置和方向）在3D空间中的对象。我们的假设是3D场景信息已重建 在V1V3ACIP路径中。我们将通过简单地记录3D姿势调整来检验这一假设 猕猴中的V3A和CIP神经元的曲线，而动物进行八个替代3D 方向区分任务。该实验将解离反映3D姿势的神经反应 基本的接收场结构（导致3D方向偏好，随着位置在深度方面变化， 我们希望从代表3D对象特征的v3a中找到它（导致3D方向 对位置不变的偏好，我们预计会在CIP中找到）。使用这些数据，我们将 另外，测试每个区域的神经活动与感知灵敏度之间的功能相关性。通过 Granger因果关系分析应用于V3A和CIP中同时的本地现场潜在记录，我们将 进一步测试进料/反馈的影响，以评估其分层结构。在 AIM 2，我们将回答双眼差异线索的问题（对象的图像下降的差异 在每个视网膜上）和透视提示（由3D世界的2D视网膜项目产生的特征） 集成在感知和神经元水平上，以实现强大的3D视觉表示。两个提示提供 有价值的3D场景信息和人类的感知研究表明，它们的整合是动态的 根据观看条件（即深度位置和深度方向）重新持续，以实现强大 3D感知。具体而言，根据观看条件将更大的权重分配给更可靠的提示。 但是，尚不清楚这个复杂的集成过程在何处以及如何在大脑中实现。我们期待 该V3A和CIP每个都会表现出对两种提示类型的敏感性，但是只有CIP会动态地将线索重新授课 实现强大的3D表示。这项研究对于理解生态相关的感觉很重要 我们成功与3D环境互动所需的处理和神经计算。 这项工作的见解还将通过阐明神经回路实施的过程超越3D愿景。 解决高度非线性优化问题，将模棱两可的感觉信号变成强大的感知。