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.

项目摘要 学习是认知基础的，需要在灵活的神经激活模式中存储信息 神经元合奏中的突触重量变化（即可塑性）。这些表示是 经过秒至几分钟甚至一生的时间尺度的经验。虽然最近 关键工作为人口活动如何推动记忆引导行为，许多人提供了见解 关于这些形成的神经可塑性机制仍然是基本问题 响应新经验的表示。标准的突触可塑性规则（即，尖峰计时 - 依赖性可塑性，STDP）需要精确的定时和重复的前和突触后激活， 这与醒着行为的动物中网络看似混乱的活动不一致。在 对比，行为时间尺度突触可塑性（BTSP），我最近共同发现了一项学习规则 是海马CA1中依赖经验的空间表示的发展的基础 只有一次归纳试验，并且在认知相关的秒数上进行操作。因此，btsp 为单一体验如何产生的生物学上有合理的机制提供了一种 与学习相关的大脑活动变化。这项先前的研究已将我的实验室定位为解决 有关电路和突触机制的基本问题。建立 我发表的工作，该提案将测试内侧内部皮层3（MEC3）的模型 作为讲师，通过其簇树突向CA1神经元提供特定于上下文的目标信号， 从而驱动BTSP并指示CA1网络如何形成与学习相关的表示。 具体而言，我们将确定MEC3如何产生此目标信号。我们将首先使用细胞外 用神经质子探针的记录来监测大量内侧种群的神经活动 在灵活的空间记忆范式中，在清醒小鼠中的内嗅皮层（MEC）神经元允许 控制学习时间课程。使用这种方法，我们将确定信息流 通过MEC网络。其次，我们将使用MEC3神经元的体内全细胞记录 相同的学习任务以查明指导信号的基础单细胞计算。我们将 确定所涉及的过程，其中可能包括兴奋性，突触输入集成和 可塑性。第三，我们将结合活动记录技术和光遗传学以确定程度 通过局部计算或从上游皮质继承而产生的指导性信号 地区。该建议将对细胞，系统和认知神经科学产生深远影响。 由于学习是几乎所有主要大脑功能的基本组成部分，因此了解神经 从突触到人群级别神经编码的学习算法将为 了解大脑如何执行依赖学习的所有复杂任务。