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

项目摘要 学习环境刺激和他们预测的强化事件之间的联系对于 生死存亡。动物和人类使用这些刺激来学习从哪里获取食物，如何避开捕食者，或者 在哪里可以找到配偶。不能预测胃口或厌恶事件的刺激之间的学习联系是 同样重要的是。虽然以前认为多巴胺信号只在以下情况下有助于学习 是刺激值的变化，最近的研究发现多巴胺神经元的激活在形成中起到了作用 中性刺激之间更复杂的认知表征。现代光遗传技术已经使 美国在有线索但正常情况下人工刺激多巴胺神经元活动 在自然主义的条件下“受阻”产生学习。从这种刺激中获得的学习 不容易被奖励预测误差所解释，这些误差只包含反映 未来事件的价值，这被认为是多巴胺的限制，而不是表明多巴胺驱动 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已经正常学习一样，尚不清楚神经印记有多接近 在这些条件下获得的是自然习得的。因此，这项提案的第一个目标是衡量 在自然刺激和人工刺激条件下学习的神经对刺激的反应。 单一单元记录在注意力、视觉处理和记忆研究中产生了变革性的信息 学习，以及其他。虽然单一单位记录明确代表神经元的输出，但更多 最近的工具，如使用基因编码的钙指示剂的钙成像，这些指示剂在 对细胞内钙离子的反应越来越多地被使用。荧光被用来“推断”神经活动 可能是也可能不是用微电极记录的相同信息。关键的是，单光子钙 在清醒的、行为正常的动物中，信号从未被直接与电生理信号进行比较。这就做 让不同组的大鼠进行相同的行为，同时用钙成像或 微电极。这个项目的第二个目标是直接比较这两个信号。 我将评估眼眶前额叶皮质的实时整体活动，这是多巴胺系统的一个皮质靶点， 支持各种形式的联想学习，在对正常条件反应与人工条件反应期间 刺激物。我将测试神经对人工学习线索的反应与类似线索的对应程度。 已经被自然地了解到了。我会直接比较电生理和单光子钙的数据。 这些实验将使我在学习理论和体内钙成像方面有很强的背景 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。这项研究的发现将揭示关于基本的 他们将为将来对钙成像数据的解释提供信息。

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.