Probing the neural representation of information acquired under the influence of midbrain dopamine transients

项目摘要 学习环境刺激和它们所预测的强化事件之间的联系是必要的 生存动物和人类利用这些刺激来学习在哪里获得食物，如何避免捕食者，或 去哪里找伴侣学习不能预测食欲或厌恶事件的刺激之间的联系， 同样重要。虽然多巴胺信号以前被认为只有助于学习时， 是刺激值的变化，最近的研究发现多巴胺神经元放电在形成 更复杂的认知表征。现代光遗传学技术 我们人工刺激多巴胺神经元活动的情况下，线索存在，但通常情况下， 在自然主义的条件下，“阻止”产生学习。从这种刺激中得到的学习 不能很容易地通过奖励预测误差来解释，这些误差只包含反映 未来事件的价值，多巴胺被认为是有限的，而是表明多巴胺驱动 获得独立于价值的感觉事件之间的联系。虽然这种人工刺激 产生的行为看起来就像动物正常学习一样，目前还不清楚神经印迹有多接近 在这些条件下获得的东西与自然学到的东西是一样的。因此，该提案的第一个目标是衡量 神经对刺激的反应是在自然和人工刺激的条件下学习的。 单单位记录在注意力、视觉处理和认知功能的研究中产生了变革性的信息。 学习，除此之外。虽然单个单位记录明确地代表了神经元的输出，但更多 最近的工具，如钙成像，使用基因编码的钙指示剂， 对细胞内钙的响应越来越多地被使用。荧光被用来“推断”神经活动， 可以是也可以不是用微电极记录的相同信息。关键是，单光子钙 信号从未被直接与清醒的、有行为的动物的电生理信号进行比较。我会 在用钙成像记录的同时，使单独的大鼠组经历相同的行为， 微电极这个项目的第二个目标是直接比较这两个信号。 我将评估眶额皮质的实时整体活动，这是多巴胺系统的皮质靶点， 支持各种形式的联想学习，在响应正常与人工条件 刺激。我将测试人工学习线索的神经反应与类似线索的对应程度， 都是自然习得的。我将直接比较电生理和单光子钙数据。 这些实验将借鉴我在学习理论和体内钙成像方面的强大背景， 在研究生院获得的。重要的是，我将利用这次培训来提高我的行为技能， 编程和分析。这项研究的结果将揭示有关基本的新信息。 相关学习的机制，他们将告知未来的钙成像数据的解释。

Project Summary Learning associations between environmental stimuli and the reinforcing events they predict is necessary for survival. Animals and humans use these stimuli to learn where to acquire food, how to avoid predators, or where to find a mate. Learning associations between stimuli that do not predict appetitive or aversive events is equally important. While dopamine signaling was previously thought to only contribute to learning when there are changes in the value of a stimulus, recent work has found a role for dopamine neuron firing in forming more complex cognitive representations between neutral stimuli. Modern optogenetic techniques have allowed us to artificially stimulate dopamine neuron activity during a situation in which cues are present but normally “blocked” from producing learning under naturalistic conditions. The learning that results from this stimulation cannot be easily accounted for by reward prediction errors that only contain scalar quantities reflecting the value of future events, which dopamine was thought to be restricted to, and instead suggests dopamine drives the acquisition of associations between sensory events independent of value. While this artificial stimulation produces behavior that appears as if animals had learned normally, it is unknown how close the neural engram acquired under these conditions is to that learned naturally. Thus, the first goal of this proposal is to measure neural responses to stimuli that were learned under naturalistic versus artificially stimulated conditions. Single unit recording has yielded transformative information in the study of attention, visual processing, and learning, among others. While single unit recording unequivocally represents the output of a neuron, more recent tools such as calcium imaging that use genetically encoded calcium indicators that fluoresce in response to intracellular calcium are increasingly being used. Fluorescence is used to “infer” neural activity that may or may not be the same information recorded with a microelectrode. Critically, single-photon calcium signals have never been directly compared to electrophysiological signals in awake, behaving animals. I will subject separate groups of rats to the same behaviors while recording with calcium imaging or microelectrodes. The second goal of this project is to directly compare these two signals. I will assess real-time ensemble activity of the orbitofrontal cortex, a cortical target of the dopamine system that supports various forms of associative learning, during responding to normally versus artificially conditioned stimuli. I will test how closely the neural responses to artificially learned cues correspond to similar cues that have been learned about naturally. I will directly compare electrophysiological and single-photon calcium data. These experiments will pull from my strong background in learning theory and in-vivo calcium imaging I acquired in graduate school. Importantly, I will use this training to enhance my skillset in behavior, programming, and analysis. Findings from this research will uncover new information about the basic mechanisms of associate learning, and they will inform future interpretation of calcium imaging data.