Circuit dynamics supporting associative learning in the dentate gyrus

项目概要 大脑将经验转化为控制情绪、决策和行为的活动模式 行为。区分这些活动模式可以将这些经验存储为 不同的实体，将重要的刺激与不重要的刺激分开，并分类为 记忆。神经科学的现代技术，例如大规模记录、计算工具 分析这些数据集和基于电路的操作提供了机会 更深入地了解学习和歧视的机制。这些 努力对人类健康至关重要，因为无法编码经验 适当的精确度是与衰老相关的认知障碍的标志。一种方法是 神经回路可以通过在群体水平上将神经网络分开来区分经验 这些学习经历的表示，允许读出区域更好地解码 来自活动模式的刺激。该计算的一个场所是海马体 (HPC)， 通过学习，编码行为相关之间的关系和区别 变量。我们最近发现海马齿状回亚区分类 嗅觉刺激的皮质表征，增加气味之间的距离 与学习相关的表征。在本提案中，我们的目标是了解这一机制 DG 中的刺激表征会随着学习而变化。在目标 1 中，我们将使用病毒式传播， 电生理学和成像工具可绘制产生气味的细胞类型和网络 在总干事中的代表。在目标 2 中，我们将确定控制的局部电路机制 气味表征与学习的灵活性，重点是多巴胺依赖性 DG GC 中编码动态的调制。在目标 3 中，我们将确定衰老如何影响 DG 中的灵活性神经表征，以及基于电路的操作如何逆转 老年小鼠的神经辨别缺陷。了解支持机制 学习引起的神经系统灵活性将促进治疗方法的开发 治疗与年龄相关的认知能力下降。

Project Summary The brain transforms experiences into patterns of activity that control emotions, decisions and behaviors. Discriminating these patterns of activity allow these experiences to be stored as distinct entities, separating important stimuli from unimportant ones, and catalogued into memory. Modern techniques in neuroscience such as large-scale recording, computational tools for analysis of these datasets and circuit-based manipulations provide an opportunity for developing a deeper understanding the mechanisms of learning and discrimination. These efforts are of profound importance to human health, as the inability to encode experiences appropriate precision is a hallmark of cognitive disorders associated with aging. One way that neural circuits may discriminate experiences is by, at the population level, separating the neural representations of these experiences with learning, allowing a readout area to better decode the stimulus from the patterns of activity. A locus of this computation is the hippocampus (HPC), which, with learning, encodes the relationships and distinctions between behaviorally relevant variables. We have recently found the dentate gyrus subregion of the hippocampus classifies cortical representations of olfactory stimuli, increasing the distance between odor representations with learning. In this proposal, we aim to understand the mechanism by which stimulus representations in the DG change with learning. In Aim 1, we will use viral, electrophysiological and imaging tools to map the cell-types and networks that generate odor representations in the DG. In Aim 2 we will determine the local circuit mechanisms that control the flexibility of odor representations with learning, with a focus on dopamine-dependent modulation of encoding dynamics in DG GCs. In Aim 3, we will determine how aging impacts the flexibility neural representations in the DG, and how circuit-based manipulations can reverse neural discrimination deficits in aged mice. Understanding the mechanisms that support learning-induced flexibility of neural ensembles will facilitate the development of therapeutics for the treatment of age-related cognitive decline.