Neural Ensemble Modulation of Learned Visual Cues across Wake and Sleep States

清醒和睡眠状态下习得视觉线索的神经集成调节

基本信息

  • 批准号:
    10405598
  • 负责人:
  • 金额:
    $ 2.36万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-05-04 至 2022-11-03
  • 项目状态:
    已结题

项目摘要

Abstract: Elucidating the neural circuitry involved in creating a learned association is crucial to understanding the biology of both typical learning and maladaptive learning. Despite decades of research into learning and memory, the mechanisms whereby newly learned associations are encoded in sensory cortices remain unclear. New functional imaging methods in neuroscience research allow for the tracking of the same groups of neurons over long time periods with single-neuron resolution, permitting us to ‘visualize’ how these memories are acquired, consolidated, and forgotten in real-time. This research will probe the neural mechanisms whereby memories are encoded in sensory association cortex with unparalleled specificity using two-photon calcium imaging, optogenetics, and advanced behavioral tracking. By utilizing visual cued fear conditioning and chronic in vivo two-photon calcium imaging in visual association cortex (VisCtx) in transgenic GCaMP6-expressing mice, we have a powerful model to track hundreds of neurons across learning processes in higher-order brain regions. Ultimately this enables us to track a neuronal ensemble representing a memory as it forms. We hypothesize that a distinct ensemble of neurons in VisCtx that represents a predicted shock-outcome will emerge during learning. In addition to local neuronal ensemble activity being important for learning and memory, sleep and long-range circuit activity is also critical to encoding long-term learned associations in the brain. Therefore, we will track neuronal ensembles representing a learned association across vigilance states, to better understand the role for sleep behavior in forming a long-term memory trace. We propose that an ensemble representing a learned association will be consolidated through reactivation during sleep post-learning. Furthermore, we propose to incorporate imaging amygdala projections to cortex to integrate circuit dynamics into our understanding of long-term storage of memory in cortex. We hypothesize that establishing a predicted-outcome responsive neuronal ensemble will be dependent on amygdala feedback to cortex after learning. Our approach allows for novel investigation of the naturalistic emergence of a learned association, including elucidating some of the circuits and behaviors that promote encoding of long-term memory in cortical circuits.
摘要: 阐明参与建立习得性联系的神经回路对于理解生物学是至关重要的 典型学习和适应不良学习。尽管对学习和记忆进行了数十年的研究,但 新学到的联系在感觉皮质中被编码的机制仍不清楚。新的 神经科学研究中的功能成像方法允许对同一组神经元进行追踪 具有单神经元分辨率的长时间段,允许我们‘可视化’这些记忆是如何获得的, 整合,并被实时遗忘。这项研究将探索记忆是如何被 利用双光子钙成像以无与伦比的特异性编码在感觉关联皮质中, 光遗传学和高级行为跟踪。 在视觉联想中利用视觉线索恐惧条件反射和慢性体内双光子钙成像 大脑皮质(VisCtx)在表达GCaMP6的转基因小鼠中,我们有一个强大的模型来跟踪数百个神经元 在更高级的大脑区域的学习过程中。最终,这使我们能够追踪神经元集合 在记忆形成时代表记忆。我们假设VisCtx中一个独特的神经元集合代表着 在学习过程中会出现一个预料到的震撼结果。除了局部神经元整体活动外, 睡眠和长程回路活动对学习和记忆很重要,对编码长时程增强也很关键 大脑中的习得联想。因此,我们将追踪代表习得联系的神经元集合。 跨越警觉状态,更好地了解睡眠行为在形成长期记忆痕迹中的作用。 我们建议,表示学习的关联的集合将通过重新激活在 学习后再睡觉。此外,我们建议合并杏仁核到皮质的成像投射,以整合 电路动力学进入了我们对记忆在大脑皮层的长期存储的理解中。我们假设 建立可预测结果的反应性神经元集合将依赖于杏仁核反馈 学习后的大脑皮质。我们的方法允许对学者的自然主义涌现进行新的调查 联系,包括阐明一些促进长期记忆编码的电路和行为 在大脑皮层环路。

项目成果

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