Project C: Neural basis of causal inference in continuous navigation

项目 C：连续导航中因​​果推理的神经基础

• 批准号: 10615056
• 负责人: Dora Angelaki
• 金额: $ 78.48万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10615056
• 关键词: Animal Behavior; Animals; Area; Behavior; Behavior monitoring; Behavioral; Behavioral Model; Belief; Brain; Brain region; Chemicals; Contralateral; Data; Decision Making; Dimensions; Dorsal; Electrodes; Electrophysiology (science); Event; Feedback; Fireflies; Goals; Joystick; Laboratories; Link; Liquid substance; Location; Macaca; Maps; Medial; Memory; Modeling; Monkeys; Motion; Mus; Muscimol; Neurons; Parietal; Pattern; Perception; Population; Process; Reporting; Retina; Rewards; Sampling; Sensory; Signal Transduction; Specific qualifier value; Speed; Structure; Testing; Time; Trackball Device Component; Training; Update; Visual Perception; computer framework; experimental study; extrastriate visual cortex; neural; neural patterning; neurophysiology; neurotransmission; novel; object motion; optic flow; optogenetics; sensory input; theories; tool; virtual reality

Project Summary When sensory inputs are ambiguous, the brain builds an internal model to infer which events in the world caused this pattern of sensory activity. This process, called causal inference, provides a unifying framework for understanding how neural signals that represent beliefs about the structure of the world interact with incoming sensory signals to drive perception-action loops. This proposal focuses on perceptual interactions among object motion, object depth, and an animal's self-motion through the world, as a particular moving pattern of neural activity on the retina can be generated by many combinations of object motion in the world and self- motion. The overall hypothesis is that parietal and prefrontal neurons infer whether an object moves in the world, and that these signals flow through feedback projections to update task-relevant representations in extrastriate visual cortex. The goal of this project is to study causal inference in dynamic tasks, in which an animal's internal model of the world changes continuously. In a virtual reality navigation task in monkeys and mice, these experiments will explore brain computation and multi-area interactions in the naturalistic setting of continuous action and active sensing, as well as dynamic on-line inference about latent, task-relevant variables related to the internal model. This project will develop a causal inference version of a dynamic navigation task already in use in the Angelaki laboratory and then use population recordings and causal neural manipulations to test and refine the dynamic model developed by the theory team in Project A. The continuous-time latent variables of this model will be fitted to monkey and mouse behavioral data to reveal each animal's beliefs about the state of the world and interacting task-relevant variables, and to generate novel hypotheses about the neural dynamics. Using multi-electrode recordings and chemical and optogenetic manipulations, this project will test these hypotheses in four mutually interconnected monkey brain areas involved in visual perception, navigation, memory, and decision-making: parietal area 7a, prefrontal area 8aV, and extrastriate visual cortical areas MSTd (dorsal medial superior temporal) and MT (middle temporal). Finally, neural activity will be mapped throughout the mouse brain, with an emphasis on subcortical structures, using parallel recordings with Neuropixels probes for hypothesis-free identification of other areas that are modulated by this dynamic task, which will also serve to generalize the findings across species. Based on these findings, additional macaque brain regions will be targeted for recording and manipulation experiments as needed. Collectively, these experiments will rigorously test the computational framework of dynamic causal inference across species and brain areas. When compared with the complementary findings from trial-based tasks in Project B, successful completion of these experiments is expected to uncover general principles of the function of causal inference processes and top-down feedback connections during naturalistic and dynamically fluid behavior.

项目摘要 当感觉输入模棱两可时，大脑会建立一个内部模型来推断世界上哪些事件 引起这种感觉活动的模式。这个过程称为因果推理，为 了解代表世界结构信念的神经信号如何与传入互动 感官信号驱动感知循环。该提案重点是之间的感知互动 物体运动，对象深度和动物在世界范围内的自我运动，作为一种特定的运动模式 视网膜上的神经活动可以通过世界上的许多物体运动和自我组合来产生 运动。总体假设是顶叶和额叶神经元推断物体是否在 世界，这些信号通过反馈预测流动以更新与任务相关的表示形式 外部视觉皮层。该项目的目的是研究动态任务中的因果推断，其中 动物的世界内部模型不断变化。在猴子和 小鼠，这些实验将在自然主义环境中探索大脑计算和多面积相互作用 连续动作和主动感应，以及对潜在任务与任务变量的动态推断 与内部模型有关。该项目将开发动态导航任务的因果推理版本 已经在Angelaki实验室中使用，然后使用人口记录和因果神经操纵 测试和完善理论团队在项目A中开发的动态模型 该模型的变量将适合猴子和鼠标行为数据，以揭示每种动物对 世界的状态和与任务相关的变量，并产生有关 神经动力学。使用多电极记录以及化学和光学遗传操作，该项目 将在参与视觉感知的四个相互关联的猴子大脑区域中检验这些假设， 导航，记忆和决策：顶区域7A，前额叶区域8av和室外视觉皮质 MSTD区域（背侧上部颞上）和MT（中间时间）。最后，神经活动将是 在整个小鼠大脑中映射，强调皮层结构，并使用与 神经质质探针，用于对通过此动态任务调节的其他领域的假设识别， 这也将有助于跨物种的发现。根据这些发现，其他猕猴 大脑区域将针对根据需要进行记录和操纵实验。总的来说，这些 实验将严格测试跨物种和 大脑区域。与项目B中基于试用任务的补充发现相比，成功 这些实验的完成有望揭示因果推断功能的一般原则 自然主义和动态流体行为期间的过程和自上而下的反馈连接。