The role of different midbrain dopamine neuron populations in signalling reward and cost

大脑必须学会使用环境线索来预测一个行动是否会产生积极或消极的后果，以及执行该行动的相关成本（例如努力）。这种类型的学习涉及到化学信使多巴胺，有很好的理论模型可以解释多巴胺释放细胞产生的神经冲动是如何发出奖励信号的。然而，尽管这些模型非常简单，但在细胞水平上却有更大的复杂性。例如，多巴胺细胞似乎由几个群体组成，它们传递奖励信号的不同方面。因此，为了更好地理解奖励过程以及这些过程何时出错（例如在成瘾中），我们必须首先了解不同的多巴胺细胞群如何向奖励信号的不同部分发出信号，以及这些信号被发送到大脑的哪个部分。为了做到这一点，我们将根据它们所支配的大脑区域划分多巴胺细胞群，并使用每个群体中存在的不同分子的组合作为一种条形码来识别它们。然后，我们将使用新开发的强大技术来记录小鼠在奖励任务中来自单个多巴胺细胞的神经冲动，并标记每个记录的神经元。我们将研究不同的多巴胺细胞群是如何发出信号的：1)积极和消极的结果；2)获得奖励所需的努力量。我们将使用每个被标记的多巴胺细胞的“条形码”来告诉我们大脑中的信号被发送到哪里。为了进一步测试不同多巴胺细胞群的作用，我们将使用尖端技术来测量特定大脑区域释放的多巴胺，然后关闭其中一个多巴胺细胞群。我们还将使用计算机模拟来帮助我们解释多巴胺神经冲动如何转化为大脑不同区域的多巴胺释放。这项研究将帮助我们更好地理解一些与奖励相关的信号的复杂性，并加强我们的学习理论模型。我们将定义多巴胺细胞群，并揭示哪些大脑区域接收不同的奖励信号成分。我们生成的“条形码”、数据和计算机模型将使我们和其他研究人员能够更好地了解奖励学习，并了解它在大脑疾病中是如何出错的。

The brain must learn to use environmental cues to predict whether an action will have positive or negative consequences and the associated cost (e.g. effort) of performing the action. This type of learning involves the chemical messenger dopamine and there are good theoretical models that explain how the nerve impulses generated by dopamine-releasing cells might signal reward. However, despite the elegant simplicity of these models, at the cellular level there is greater complexity. For example, dopamine cells seem to consist of several populations which convey different aspects of the reward signal. Therefore, to better understand reward processes and when these go awry (e.g. in addiction), we must first understand how different dopamine-cell populations signal different parts of the reward signal and to which parts of the brain these signals are sent. To do this, we will divide populations of dopamine cells according to the brain regions that they innervate and use the combination of different molecules present in each population as a kind of barcode to identify them. We will then use newly-developed, powerful techniques to record nerve impulses from individual dopamine cells in mice during a reward-task and label each recorded neuron. We will investigate how different populations of dopamine cells signal 1) positive and negative consequences and 2) the amount of effort required to obtain reward. We will use each labelled dopamine cell's 'barcode' to tell us where in the brain the signals were sent. To further test the role of different dopamine-cell populations, we will use cutting-edge technologies to measure dopamine released in a particular brain-region and then switch-off one of the dopamine-cell populations. We will also use computer simulations to help us interpret how the dopamine nerve-impulses translate into dopamine release in different regions of the brain. This research will help us to better understand some of the complexity of reward-related signalling and enhance our theoretical models of learning. We will define populations of dopamine cells and reveal which brain regions receive different components of the reward signal. The 'barcodes', data, and computer models we generate will enable us and other researchers to build a better picture of reward learning and understand how it goes wrong in brain disorders.