Tracking the emergence of internal models

追踪内部模型的出现

• 批准号: 10429372
• 负责人: Elizabeth A Buffalo
• 金额: $ 620.64万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10429372
• 关键词: Address; Anatomy; Animals; Architecture; Area; Behavior; Behavioral; Brain; Brain region; Buffaloes; Code; Cognition; Collaborations; Complex; Computer Models; Cues; Data; Data Analyses; Diagnosis; Environment; Episodic memory; Foundations; Goals; Hippocampus (Brain); Human; Individual; Knowledge; Learning; Location; Macaca; Maintenance; Memory; Mental disorders; Modeling; Monkeys; Motion; Motor; Motor Cortex; Movement; Nature; Perception; Poverty; Prefrontal Cortex; Primates; Recurrence; Role; Sensory; Series; Statistical Models; Structure; Technology; Training; Variant; Work; brain dysfunction; cognitive ability; entorhinal cortex; experience; inferotemporal cortex; knowledge repository; network models; neurophysiology; nonhuman primate; novel; predictive modeling; relating to nervous system; theories; visual information

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?

摘要