Decoding the dynamic representation of reward predictions across mesocorticostriatal circuits during learning

奖励学习是一种基本的认知功能，大脑有一个专门的神经调节系统， 基于多巴胺的系统支持这一过程。多巴胺系统的变化 暴露于滥用药物引发的行为改变被认为是在 成瘾在这里，我们建议使用以前记录的神经数据宝库， 在支持奖励学习的中皮质纹状体回路中， 电路的每个组件的计算角色，它们的相互作用，以及这些组件如何 受到可卡因的影响。 我们的大脑不断地产生关于可能获得的奖励的预测， 这些预测到实际结果。神经调节剂多巴胺被认为是报告这些 “预测误差”信号，预期和获得之间的持续比较的结果 奖励，这是更新预测的关键，因此它们在未来更准确。预测 奖励的时机，而不仅仅是他们的身份或价值，是这个过程的重要组成部分，但它 大脑如何在奖励学习中形成和使用关于时间的预测仍然是一个谜。 基于我们最近开发的一个新的理论模型，我们将测试 三个关键的大脑区域，包括大脑回路的关键奖励学习，使用的状态- 机器学习的艺术方法，共同解码驱动神经活动的学习过程 从多个大脑区域沿着与行为作为大鼠执行奖励学习任务。目标1： 假设眶额皮质神经活动对于代表高血压是非常重要的， 水平的“任务状态”，并将测试OFC神经活动的模式，遵循隐藏的结构， 任务在目标2中，我们将解码奖励预测的表示， 奖励，并测试它们是否在VS神经活动中是可分离的。在目标3中，我们将测试如何 VS和OFC中的活动控制多巴胺的活动，特别是每个输入成分 使得预测误差在时间上精确。在目标4中，我们将测试接触可卡因 改变代表VS中奖励预测的神经活动，以及这种中断的影响 关于腹侧被盖区多巴胺预测错误的研究 这项创新的多层次研究将利用大量现有的大鼠神经和行为数据 执行一个有效的奖励学习任务，以揭示计算，神经和行为 大脑中的奖励预测和学习电路的机制，以及它们的来源。 成瘾的破坏。

Reward learning is a fundamental cognitive function, and the brain has a dedicated neuromodulatory system – based on dopamine – that supports this process. Changes to the dopamine system that are triggered by exposure to drugs of abuse are thought to underlie the behavioral changes observed in addiction. Here we propose to use a treasure trove of previously recorded neural data from throughout the mesocorticostriatal circuitry that supports reward learning, to elucidate the computational role of each component of the circuit, their interactions, and how these components are affected by cocaine. Our brains constantly generate predictions about what rewards might be available, and compare these predictions to actual outcomes. The neuromodulator dopamine is thought to report these ‘prediction error’ signals, the result of the ongoing comparison between expected and obtained rewards, that are key to updating predictions so they are more accurate in the future. Predicting the timing of rewards, and not just their identity or value, is an important component of this process, but it remains a mystery how the brain forms and uses predictions about time in reward learning. Based on a novel theoretical model we recently developed, we will test the computational role of three key brain areas that comprise the brain circuit critical for reward learning, using a state-of-the- art methods from machine learning to jointly decode the learning processes that drive neural activity from multiple brain areas along with behavior as rats perform a reward learning task. In Aim 1, we hypothesize that neural activity in the orbitofrontal cortex is uniquely important for representing high level ‘task states’ and will test for patterns in OFC neural activity that follow the hidden structure of the task. In Aim 2, we will decode the representation of reward predictions about the amount and timing of rewards, and test whether they are separable in VS neural activity. In Aim 3, we will test how activity in VS and OFC controls dopamine activity, and in particular how each input component enables prediction errors to be temporally precise. In Aim 4, we will test how exposure to cocaine changes neural activity that represents reward predictions in the VS, and the impact of this disruption on dopamine prediction errors in the VTA. This innovative multi-level study will leverage numerous existing neural and behavioral data from rats performing a well-validated reward-learning task, to reveal the computational, neural and behavioral mechanisms of the reward prediction and learning circuitry in the brain, and the source of their disruption in addiction.