Neural population dynamics in premotor cortex during decision making

决策过程中前运动皮层的神经群体动态

• 批准号: 10018951
• 负责人: Matthew D. Golub
• 金额: $ 11.83万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10018951
• 关键词: Anxiety; Area; Attention; Automobile Driving; Behavior; Bipolar Disorder; Brain; Collection; Color; Data; Data Set; Decision Making; Diagnosis; Discrimination; Dorsal; Engineering; Ensure; Functional disorder; Goals; Human; Impairment; Individual; Knowledge; Lead; Learning; Link; Mental Depression; Mental disorders; Modality; Modeling; Monkeys; Motion; Names; Neurons; Obsessive-Compulsive Disorder; Participant; Phase; Population; Population Dynamics; Process; Reporting; Research; Role; Sensory; Sensory Process; Speed; Stimulus; Structure; Techniques; Testing; Time; Training; Variant; analytical tool; artificial neural network; behavioral response; experimental study; flexibility; improved; innovation; multiple datasets; multitask; neural circuit; neuromechanism; neurophysiology; nonhuman primate; novel; public health relevance; recurrent neural network; relating to nervous system; 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 决策需要大脑中的神经元群体集体处理感觉证据并选择 适当的行为反应。神经种群动力学(NPD)，它描述了一个人的反应如何 随着时间的推移，个体神经元的数量可以为这些决策过程提供一个有洞察力的观点。 关于单个神经元在特定的决策任务中是如何反应的，我们已经知道了很多。然而，人们对此知之甚少 关于神经元群体如何动态执行决策计算，以及在 大脑区域在许多决策任务中都有结构。NPD中跨多个任务的共享功能可能 指出作为给定神经回路的多功能基础的统一的神经计算机制。 这项提案旨在揭示人类和非人类中这些与决策相关的NPD的细节和结构 灵长类背侧运动前皮质，这一区域与决策的功能和功能障碍密切相关。 将特别注意审查如何跨多项决策任务组织新的决策任务 以及在学习新任务的过程中这些NPD是如何出现的。在K99阶段，新的分析工具将是 开发用于从同时记录的神经群体活动中提取NPD，重要的是，用于 提供NPD与其在与决策相关的神经计算中的作用之间的可解释链接。带着目标 在确定与决策相关的计算的统一原则之后，大规模分析将整合现有的 以及新收集的涉及人类执行的多个决策任务的神经生理学数据集 以及非人灵长类动物。在R00阶段，拟议的研究将侧重于了解 在学习如何做出新的决策时，NPDS应运而生。这项提议假定，轻松、快速、 学习的效率取决于关键神经回路可以利用预先存在的神经的程度 计算机制。这些概念将在协作的人类和非人类灵长类神经元上进行测试。 生理实验，通过对人工神经网络的询问提供指导 类似的决策相关学习任务。完成后，拟议的研究将提供新的基础 关于i)运动前皮质在许多决策中的多功能和适应性作用的知识 任务和ii)统一支持这种灵活性的神经计算原理。更好地理解决策- 相关回路及其神经机制可能最终阐明众多精神疾病的基础 损害决策的障碍，最终可能导致改善对这些疾病的诊断和治疗 令人衰弱的条件。