Brain-wide neural population dynamics during decision making and learning

决策和学习过程中的全脑神经群体动态

• 批准号: 10732472
• 负责人: Matthew D. Golub
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-06 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10732472
• 关键词: Anxiety; Area; Attention; Automobile Driving; Behavior; Bipolar Disorder; Brain; Collection; Color; Data; Data Collection; Data Set; Decision Making; Diagnosis; Discrimination; Dorsal; Ensure; Functional disorder; Goals; Human; Impairment; Individual; Knowledge; Learning; Link; Mental Depression; Mental disorders; Modality; Modeling; Monkeys; Motion; Motor Cortex; Neurons; Obsessive-Compulsive Disorder; Participant; Phase; Population; Population Dynamics; Process; Reporting; Research; Reverse engineering; Role; Sensory; Sensory Process; Speed; Stimulus; Structure; Techniques; Testing; Time; Training; Variant; analytical tool; artificial neural network; behavioral response; experimental study; flexibility; improved; innovation; learning network; multiple datasets; multitask; neural; neural circuit; neuromechanism; neurophysiology; nonhuman primate; novel; public health relevance; recurrent neural network; response; tool

Project Summary / Abstract Decision-making requires populations of neurons in the brain to collectively process sensory evidence and select appropriate behavioral responses. Neural population dynamics (NPDs), which describe how the responses of a population of individual neurons unfold over time, can provide an insightful view into these decision processes. Much is known about how individual neurons respond in select decision making tasks. However, little is known about how populations of neurons dynamically perform decision computations, and how resulting NPDs within a brain area are structured across many decision-making tasks. Shared features in NPDs across many tasks could indicate unifying neural mechanisms of computation that underlie the multi-functionality of a given neural circuit. This proposal aims to uncover the details and structure of these decision-related NPDs in human and nonhuman primate dorsal premotor cortex, an area tightly linked to both the function and dysfunction of decision making. Particular attention will be devoted to examining how NPDs are organized across multiple decision-making tasks and how those NPDs emerge during learning of new tasks. During the K99 phase, novel analytical tools will be developed for extracting NPDs from simultaneously recorded neural population activity and, importantly, for providing interpretable links between NPDs and their role in decision-related neural computation. With the goal of identifying unifying principles of decision-related computation, large-scale analyses will then integrate existing and newly collected neurophysiological datasets involving multiple decision-making tasks performed by humans and nonhuman primates. During the R00 phase, the proposed research will pivot toward understanding how NPDs emerge during learning to make new types of decisions. The proposal postulates that the ease, speed, and efficacy of learning all hinge on the extent by which critical neural circuits can leverage pre-existing neural mechanisms of computation. These concepts will be tested in collaborative human and nonhuman primate neu- rophysiological experiments, with guidance provided by interrogations of artificial neural networks posed with similar decision-related learning tasks. Upon completion, the proposed research will provide new fundamental knowledge concerning i) the multi-functional and adaptive role of premotor cortex across many decision-making tasks and ii) unifying principles of neural computation that support this flexibility. Better understanding decision- related circuits and their neural mechanisms could ultimately elucidate the basis for the numerous psychiatric disorders that impair decision making, which could eventually lead to improved diagnosis and treatment of these debilitating conditions.

项目摘要/摘要