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: 10225405
• 负责人: Dora Angelaki
• 金额: $ 89.85万
• 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/10225405
• 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 Cortex; Visual Perception; area MST; base; computer framework; experimental study; extrastriate; extrastriate visual cortex; neural patterning; neurophysiology; neurotransmission; novel; object motion; optic flow; optogenetics; relating to nervous system; 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.

项目概要 当感觉输入不明确时，大脑会建立一个内部模型来推断世界上发生的哪些事件 造成了这种感觉活动模式。这个过程称为因果推理，为以下问题提供了一个统一的框架： 了解代表关于世界结构的信念的神经信号如何与传入的相互作用 驱动感知-行动循环的感觉信号。该提案侧重于之间的感知交互 物体运动、物体深度以及动物在世界中的自我运动，作为物体的特定移动模式 视网膜上的神经活动可以通过世界物体运动和自我运动的多种组合来产生。 运动。总体假设是，顶叶和前额叶神经元推断物体是否在大脑中移动。 世界，并且这些信号流过反馈投影来更新任务相关的表示 纹状体外视觉皮层。该项目的目标是研究动态任务中的因果推理，其中 动物的世界内部模型不断变化。在猴子的虚拟现实导航任务中 小鼠，这些实验将探索自然环境中的大脑计算和多区域交互 连续动作和主动感知，以及关于潜在的、任务相关变量的动态在线推理 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 实验室使用，然后使用群体记录和因果神经操作 测试和完善项目 A 理论团队开发的动态模型。连续时间潜伏 该模型的变量将适合猴子和小鼠的行为数据，以揭示每种动物的信念 世界的状态和相互作用的任务相关变量，并产生关于 神经动力学。该项目使用多电极记录以及化学和光遗传学操作 将在涉及视觉感知的四个相互关联的猴子大脑区域中测试这些假设， 导航、记忆和决策：顶叶区 7a、前额叶区 8aV 和纹状外视觉皮层 区域 MSTd（颞上内侧背侧）和 MT（颞中）。最后，神经活动将 使用并行记录绘制整个小鼠大脑的图谱，重点是皮层下结构 Neuropixels 探针用于无假设地识别受此动态任务调节的其他区域， 这也将有助于推广跨物种的发现。根据这些发现，额外的猕猴 根据需要，大脑区域将成为记录和操作实验的目标。总的来说，这些 实验将严格测试跨物种动态因果推理的计算框架 大脑区域。与项目 B 中基于试验的任务的补充结果相比，成功 完成这些实验预计将揭示因果推理功能的一般原理 自然和动态流体行为期间的过程和自上而下的反馈连接。