Learning in neural circuits: applied optogenetics in non-genetic models

神经回路学习：光遗传学在非遗传模型中的应用

• 批准号: 7852872
• 负责人: MICHAEL S BRAINARD
• 金额: $ 161.69万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7852872
• 关键词: Animals; Area; Behavior; Behavioral; Biological Models; Birds; Brain; Brain region; Cells; Genetic Materials; Genetic Models; Goals; Halorhodopsins; Hippocampus (Brain); Ion Channel; Knowledge; Learning; Light; Macaca; Maps; Measures; Modification; Monkeys; Movement; Mus; Nature; Neural Pathways; Neuromodulator; Neurons; Neurosciences; Optics; Pathway interactions; Pattern; Performance; Population; Production; Property; Protocols documentation; Rattus; Relative (related person); Research; Research Personnel; Rewards; Sensory; Signal Transduction; Site; Songbirds; Synapses; Techniques; Technology; Testing; Universities; Upper arm; Virus; Work; base; cell type; experience; genetic manipulation; in vivo; indexing; millisecond; nervous system disorder; neural circuit; neural patterning; neuroregulation; new technology; non-genetic; nonhuman primate; novel strategies; relating to nervous system; sensory feedback; tool; tool development; virus development

DESCRIPTION (provided by applicant): While great advances have been made in understanding the mechanisms of learning in the single synapse or cell, a large gap remains between this understanding and our knowledge of learning at the behavioral level. We know that the activity of large-scale neuronal circuits gives rise to behavior, yet we have little knowledge of what changes in those circuits during learning or how sensory feedback drives these changes. The biggest impediment to answering these questions is the inability to quantitatively measure large-scale circuit properties (e.g. connectivity between brain areas) or to precisely manipulate the activity patterns across these circuits. Optogenetics offers the potential to bridge this gap by allowing the direct control of neural activation in targeted cell types on the millisecond timescale. The development of these tools is progressing most rapidly in mouse, due to the relative ease of genetic manipulations in that species. In contrast, behavioral and circuit-level studies of learning are most practical and have been most successful in "non-genetic" species. Within our team, we have expertise in studying both the behavioral and neural bases of learning in rat, songbird, and nonhuman primate. We propose to develop the optogenetic tools and experimental techniques required to study the circuit-level mechanisms of learning in these species and to apply these to two specific scientific aims: Aim 1: Determine the functional connectivity of learning-related circuitry and how it is altered by experience. It is widely presumed that learning relies on the ability of instructive signals to drive functional modifications of connectivity in the circuits that underlie behavior. However, the tools for measuring functional connectivity in vivo have been limited. We will overcome this limitation using temporally and/or spatially precise optical activation of neurons within a circuit. Functional connectivity will be measured by recording optical-stimulation-triggered changes in activity in downstream neurons. We will assess how functional connectivity is dynamically altered by learning and by factors that may contribute crucially to learning. Connectivity changes will serve as a mechanistic index of the nature and sites of the plasticity that give rise to behavioral change. Aim 2: Test the causal contributions of patterned activity to learning in vivo. Prior research has generated specific and testable hypotheses about how and where patterned activity drives learning. Yet support for these hypotheses has derived primarily from correlative observations of activity during learning rather than causal tests of the proposed mechanisms. We will use optogenetics to causally test the contributions of patterned activity to learning. We will test the sufficiency of instructive signals by imposing precisely controlled patterns of activity at defined loci in a circuit and test their necessity by eliminating the putative signals for learning. PROJECT NARRATIVE This project is aimed at revolutionizing the study of the mechanisms of learning within large neural circuits in the brain by directly measuring large-scale properties of these circuits and precisely manipulating circuit activity. To accomplish this, we will make use of, and continue to develop, advanced new techniques that permit the control of specific population of neurons using optical stimulation (light). The knowledge and tools that we gain from these studies are likely to find broad application in the search for treatments of neurological disorders.

描述(由申请人提供)： 虽然在理解单个突触或细胞中的学习机制方面已经取得了很大的进展，但这种理解与我们在行为水平上的学习知识之间仍然存在很大的差距。我们知道，大规模神经元回路的活动会引起行为，但我们对这些回路在学习过程中发生了什么变化，或者感觉反馈如何驱动这些变化知之甚少。回答这些问题的最大障碍是无法定量测量大规模电路属性(例如，大脑区域之间的连通性)或精确操纵这些电路的活动模式。光遗传学通过允许在毫秒时间尺度上直接控制目标细胞类型的神经激活，提供了弥合这一差距的潜力。这些工具的发展在小鼠身上进展最快，这是因为该物种的基因操作相对容易。相比之下，对学习的行为和回路水平的研究是最实用的，也是在“非遗传”物种中最成功的。在我们的团队中，我们在研究大鼠、鸣禽和非人类灵长类动物学习的行为和神经基础方面拥有专业知识。我们建议开发研究这些物种学习电路水平机制所需的光遗传学工具和实验技术，并将这些工具和实验技术应用于两个特定的科学目标：目标1：确定与学习相关的电路的功能连通性以及它是如何被经验改变的。人们普遍认为，学习依赖于指导性信号的能力，以驱动构成行为基础的电路中连通性的功能性改变。然而，用于测量体内功能连接的工具一直是有限的。我们将通过在时间和/或空间上精确地激活电路内的神经元来克服这一限制。功能连通性将通过记录光刺激触发的下游神经元活动的变化来衡量。我们将评估学习以及可能对学习起关键作用的因素如何动态改变功能连接。连接性变化将作为导致行为变化的可塑性的性质和位置的机械指标。目的2：在活体内检验模式化活动对学习的因果贡献。先前的研究已经产生了关于模式化活动如何以及在哪里推动学习的具体和可检验的假说。然而，对这些假设的支持主要来自对学习过程中活动的相关观察，而不是对所提出的机制的因果测试。我们将使用光遗传学来因果检验模式化活动对学习的贡献。我们将通过在电路中定义的位置施加精确控制的活动模式来测试指示信号的充分性，并通过消除用于学习的假定信号来测试它们的必要性。 项目简介该项目旨在通过直接测量大脑中大型神经回路的大规模特性和精确操纵回路活动来革命性地研究大脑中大型神经回路的学习机制。为了实现这一点，我们将利用并继续开发先进的新技术，这些技术允许使用光刺激(光)来控制特定数量的神经元。我们从这些研究中获得的知识和工具可能会在寻找神经疾病的治疗方法方面得到广泛的应用。