Tracking the emergence of internal models

摘要 人类和动物认知的核心是内部模型的概念：内部模型的内部存储库 关于世界结构及其负担能力的知识，使预测和 计划。这些模型的存在是经验的基础。当我们穿行在 世界上，我们接收到的原始瞬时感觉信息是高度贫乏的 动感相对丰富、有条理、稳定、细腻的体验。已学习并已 也许部分先天的先验能够维持对世界的真实再现 以及快速选择和整合与任务相关的重要信息。在这 提案中，我们的目标是通过记录来自多个 灵长类动物在自然环境中航行时的大脑区域。通过与 分析单个复杂序列期间多个实验室的协调记录 任务，我们将全面了解这些模型是如何发展的，依赖于Active 与世界接触，影响人们的认知。在我们的项目中，三只猕猴 神经生理学实验室，在不同的大脑区域和 技术，将与具有不同专业知识的计算神经科学家合作 追求重叠的目标。我们提出了一种新的协作策略，在该策略中，三只猕猴 神经生理学实验室将研究一项常见的导航任务，并从不同的 重叠的区域子集：颞下皮质(IT)、运动皮质(MC)和前额叶 皮层(PFC)。我们最初专注于IT-HC(Tsao)、MC-HC(Orsborne)和PFC-HC(Buffalo)， 假设根据它的解剖学，它在情景记忆中的基本功能 队形及其在编排主观体验中的主要作用，构成了一个中心枢纽 代表了大脑对世界的内部模型。为了帮助解读这些新数据，我们将 与分析协作开发和探索预测处理的网络模型 和理论团队。FairHall、Mihalas、Rao和Shea-Brown拥有互补的专业知识 神经编码、预测编码和网络动力学将相互作用，发展综合 建立框架模型，并单独与每个实验实验室合作进行数据分析。主修 我们将讨论的问题包括：这样的“世界模式”是如何发展的？它是如何发展的？ 在大脑中的表现吗？世界模型对经济发展的影响是什么 感官处理和行为？习得的结构性知识如何与自我整合 在环境探索过程中的运动？在记忆过程中，这些信息是如何被提取的- 引导式导航到达目标？

Abstract Central to human and animal cognition is the idea of internal models: an internal repository of knowledge about the structure of the world and its affordances that enables prediction and planning. The existence of such models is fundamental to experience. As we move through the world, the raw instantaneous sensory information that we receive is highly impoverished and dynamic relative to the rich, organized, stable and detailed nature of experience. Learned and perhaps partially innate priors allow the maintenance of a veridical representation of the world around us, and rapid selection and integration of important information relevant to a task. In this proposal, we aim to probe the neural implementation of world models by recording from multiple brain areas in primates as they navigate naturalistic environments. Through modeling alongside analysis of coordinated recordings across multiple labs during a single sequence of complex tasks, we will develop a holistic understanding of how such models develop, depend on active engagement with the world, and influence perception. In our project, three macaque neurophysiology labs, with distinct cutting-edge expertise in different brain regions and technologies, will collaborate with computational neuroscientists with different expertise to pursue overlapping aims. We propose a novel collaboration strategy in which all three macaque neurophysiology labs will investigate a common navigation task, and record from different but overlapping subsets of areas: inferotemporal cortex (IT), motor cortex (MC) and prefrontal cortex (PFC). We focus initially on IT-HC (Tsao), MC-HC (Orsborne), and PFC-HC (Buffalo), on the hypothesis that HC, by virtue of its anatomy, its essential function in episodic memory formation, and its master role in orchestrating subjective experience, constitutes a central hub for representing the brain’s internal model of the world. To help to interpret this new data, we will develop and probe network models of predictive processing in collaboration with the analysis and theory team. Fairhall, Mihalas, Rao, and Shea-Brown, with complementary expertise in neural coding, predictive coding, and network dynamics, will interact to develop integrated model frameworks and work with each experimental lab individually on data analysis. Major questions we will address include: How does such a “world model” develop and how is it represented in the brain? What are the consequences of world models for the dynamics of sensory processing and behavior? How is learned structural knowledge integrated with self- motion during exploration of an environment? How is this information retrieved during memory- guided navigation to a goal?