Unraveling the synaptic and circuit mechanisms underlying a plasticity-driving instructive signal

揭示可塑性驱动指导信号背后的突触和电路机制

• 批准号: 10686592
• 负责人: Christine Grienberger
• 金额: $ 141.52万
• 依托单位: BRANDEIS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-21 至 2026-08-20
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10686592
• 关键词: Address; Affect; Alzheimer' s Disease; Alzheimer' s disease patient; American; Animals; Automobile Driving; Behavior; Behavioral; Body Weight Changes; Brain; Brain Diseases; Cells; Code; Cognition; Cognitive; Complex; Dendrites; Development; Hippocampal Formation; Hippocampus; Impaired cognition; Inherited; Laboratories; Learning; Medial; Memory; Modeling; Monitor; Mus; Neuronal Plasticity; Neurons; Pattern; Population; Positioning Attribute; Process; Publishing; Research; Role; Signal Transduction; Synapses; Synaptic plasticity; System; Techniques; Testing; Time; Whole-Cell Recordings; Work; awake; cognitive neuroscience; effective therapy; entorhinal cortex; experience; extracellular; flexibility; human old age (65+); in vivo; insight; instructor; learning algorithm; neural; optogenetics; postsynaptic; presynaptic; response; spatial memory; virtual

PROJECT SUMMARY Learning, fundamental to cognition, requires storing of information in flexible neural activation patterns and synaptic weight changes (i.e., plasticity) within neuronal ensembles. These representations are modified with experience on the timescale of seconds to minutes and even lifetimes. Although recent pivotal work has provided insights into how population activity drives memory-guided behaviors, many fundamental questions remain about the neural plasticity mechanisms that underlie the formation of these representations in response to new experiences. The standard synaptic plasticity rule (i.e., spike timing- dependent plasticity, STDP) requires precisely timed and repetitive pre- and postsynaptic activation, which is incongruent with the seemingly chaotic activity of networks in awake behaving animals. In contrast, behavioral timescale synaptic plasticity (BTSP), a learning rule I recently co-discovered to underlie the development of experience-dependent spatial representations in hippocampal CA1, requires only a single induction trial and operates on the cognitively relevant timescale of seconds. Thus, BTSP provides one of the first biologically plausible mechanisms for how a single experience can produce learning-related changes in brain activity. This previous research has positioned my laboratory to address fundamental questions regarding the circuit and synaptic mechanisms underlying learning. Building upon my published work, this proposal will test the model that the medial entorhinal cortex layer 3 (mEC3) serves as an instructor, providing a context-specific target signal to CA1 neurons via their tuft dendrites, thereby driving BTSP and directing the CA1 network in how to form a learning-related representation. Specifically, we will determine how the mEC3 produces this target signal. We will first use extracellular recordings with Neuropixels probes to monitor the neural activity from large populations of medial entorhinal cortex (mEC) neurons in awake mice during a flexible spatial memory paradigm that allows control over the learning time course. Using this approach, we will determine the flow of information through the mEC network. Second, we will use in vivo whole-cell recordings of mEC3 neurons during the same learning task to pinpoint the single-cell computations underlying the instructive signal. We will identify the processes involved, which may include changes in excitability, synaptic input integration, and plasticity. Third, we will combine activity recording techniques and optogenetics to determine the extent to which the instructive signal is produced by local computation or inherited from upstream cortical regions. This proposal will have a far-reaching influence on cellular, systems, and cognitive neuroscience. As learning is a fundamental component of virtually all major brain functions, understanding the neural algorithms of learning, from synaptic to population level neural coding, will provide a basis for understanding how the brain performs all complex tasks that depend upon learning.

项目概要 学习是认知的基础，需要以灵活的神经激活模式存储信息 以及神经元群内突触重量的变化（即可塑性）。这些表示是 根据经验在秒到分钟甚至一生的时间尺度上进行修改。虽然最近 关键工作提供了关于人口活动如何驱动记忆引导行为的见解，许多 关于形成这些神经可塑性机制的基本问题仍然存在 对新经历的反应。标准突触可塑性规则（即尖峰计时- 依赖可塑性，STDP）需要精确定时和重复的突触前和突触后激活， 这与清醒行为动物看似混乱的网络活动不一致。在 相比之下，行为时间尺度突触可塑性（BTSP）是我最近与他人共同发现的一种学习规则 是海马 CA1 中依赖于经验的空间表征发展的基础，需要 仅进行一次诱导试验，并在与认知相关的秒级时间尺度上进行。因此，BTSP 提供了第一个生物学上合理的机制之一，说明单一经验如何产生 与学习相关的大脑活动变化。之前的研究使我的实验室能够解决 有关学习背后的电路和突触机制的基本问题。建立在 我发表的作品，该提案将测试内侧内嗅皮质第 3 层（mEC3）的模型 作为指导者，通过 CA1 神经元的簇状树突向 CA1 神经元提供上下文特定的目标信号， 从而驱动 BTSP 并指导 CA1 网络如何形成与学习相关的表示。 具体来说，我们将确定 mEC3 如何产生该目标信号。我们首先使用细胞外 使用 Neuropixels 探针进行记录，以监测大量内侧神经活动 清醒小鼠的内嗅皮层（mEC）神经元在灵活的空间记忆范例中允许 控制学习时间进程。使用这种方法，我们将确定信息流 通过 mEC 网络。其次，我们将在实验过程中使用 mEC3 神经元的体内全细胞记录。 相同的学习任务来查明指导信号背后的单细胞计算。我们将 识别所涉及的过程，其中可能包括兴奋性、突触输入整合的变化，以及 可塑性。第三，我们将结合活动记录技术和光遗传学来确定程度 指导信号由本地计算产生或从上游皮质继承 地区。这一提议将对细胞、系统和认知神经科学产生深远的影响。 由于学习几乎是所有主要大脑功能的基本组成部分，因此了解神经元 从突触到群体水平神经编码的学习算法将为 了解大脑如何执行所有依赖于学习的复杂任务。