Circuit dynamics supporting associative learning in the dentate gyrus

项目摘要 大脑将经验转化为活动模式，控制情绪，决策和行为。 行为。区分这些活动模式可以让这些经验存储为 不同的实体，将重要的刺激与不重要的刺激分开，并归类为 记忆神经科学中的现代技术，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一个机会， 加深对学习和歧视机制的理解。这些 这些努力对人类健康具有深远的重要性，因为无法对经验进行编码 适当的精确度是与衰老相关的认知障碍的标志。的一种方式 神经回路可以区分经验的方法是，在群体水平上， 这些经验与学习的表征，允许读出区域更好地解码 从活动模式的刺激。这种计算的一个位点是海马体（HPC）， 通过学习，它编码了行为相关的 变量我们最近发现海马齿状回亚区分为 嗅觉刺激的皮层表征，增加气味之间的距离 表征与学习。在本建议中，我们旨在了解 DG中的刺激表征随着学习而改变。在目标1中，我们将使用病毒， 电生理学和成像工具来绘制产生气味的细胞类型和网络 在DG的代表。在目标2中，我们将确定控制 气味表征的灵活性与学习，重点是多巴胺依赖 DG GC中编码动力学的调制。在目标3中，我们将确定衰老如何影响 DG中的灵活性神经表示，以及基于电路的操作如何逆转 老年小鼠的神经辨别缺陷。了解支持的机制 学习诱导的神经系统的灵活性将促进治疗的发展， 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.