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: 9769032
• 负责人: Ari Rosenberg
• 金额: $ 42.03万
• 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/9769032
• 关键词: 3-Dimensional; Animals; Area; Behavioral; Brain; Brain region; Clutterings; Code; Complex; Cues; Data; Dimensions; 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; Retinal; 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.

项目总结/摘要 当我们的眼睛只能感知二维时，我们如何感知世界的三维（3D）结构 (2D)在屏幕上放映电影从2D视网膜图像重建3D场景信息是一种有效的方法。 这是一个高度复杂的问题，机器人在将视觉输入转换为适当的 3D电机输出在杂乱的棋盘上移动物理棋子，即使它们可以击败最好的人类 国际象棋选手该提议的目标是阐明分层皮层电路如何实现鲁棒的（即， 准确和精确）3D视觉感知。为此，我们将回答两个基本问题， 大脑是如何利用行为、电生理和神经学来实现2D到3D的视觉转换的， 成像方法。在目标1中，我们将回答视觉系统如何表示空间姿态的问题 (i.e.,位置和方向）。我们的假设是，3D场景信息被重建 在V1-V3 A-CIP途径中。我们将通过同时记录3D姿态调整来测试这一假设 猕猴的V3 A和CIP神经元的曲线，而动物执行八选择3D 方向辨别任务。该实验将分离对3D姿势的神经反应， 基本感受野结构（导致随着深度位置而变化的3D取向偏好， 我们预期在V3 A中找到）与表示3D对象特征（导致3D定向）的那些特征相比较 对深度位置不变的偏好，我们期望在CIP中找到）。利用这些数据，我们将 此外，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 应用格兰杰因果关系分析，同时在V3 A和CIP的局部场电位记录，我们将 进一步测试区域之间的前馈/反馈影响，以评估其层次结构。在 目标2，我们将回答双眼视差如何提示（物体图像福尔斯落在何处的差异）的问题 在每个视网膜上）和透视线索（由3D世界的2D视网膜投影产生的特征）是 在感知和神经元层面进行整合，以实现强大的3D视觉表示。这两个线索都提供了 有价值的3D场景信息，人类感知研究表明，它们的集成是动态的， 根据观看条件重新加权（即，深度定位和深度定向），以实现鲁棒性 3D感知具体来说，根据观看条件，将更大的权重分配给更可靠的提示; 但是，这种复杂的整合过程在大脑中的何处以及如何实现尚不清楚。我们预计 V3 A和CIP将分别显示对两种线索类型的敏感性，但只有CIP将动态地重新加权线索， 实现鲁棒的3D表示。这项研究对于理解生态相关的感官具有重要意义。 处理和神经计算是我们成功地与3D环境交互所必需的。 这项工作的见解也将通过阐明神经电路实现的过程而扩展到3D视觉之外 解决高度非线性优化问题，将模糊的感官信号转化为强大的感知。