The role of cerebellum in dopamine neuron reward prediction error coding

做出和评估预测是大脑功能的一个基本特征。所有动物都从经验中学习，以优化它们的行为以求生存，但我们不知道大脑中的关键“预测节点”如何相互交谈，也不知道对话是什么。想想冰淇淋车上熟悉的叮当声；我们的大脑已经从经验中了解到，这种声音标志着冰淇淋的供应，以及冰淇淋的美味程度，指导我们做出接近或避开冰淇淋的决定。小脑和腹侧被盖区(VTA)是预测的关键中枢，但到目前为止一直被认为是独立的系统。新的证据显示，从小脑到VTA的显著联系可以影响奖赏行为，从而1)重新定义我们对这些系统功能的看法，2)表明预测可能在整个大脑中得到协调。这项建议的目的是提供一个详细的电路级特征的小脑在编码奖励预测误差在VTA中的作用。为了实现这一点，我们将首先将小脑输入的组织映射到VTA内的多巴胺能和非多巴胺能神经元。其次，我们将通过记录奖励预测行为中的投射神经元来定义VTA的小脑信号的性质。第三，我们将在行为过程中选择性地干扰这些输入，以确定小脑如何形成奖励VTA中的预测错误编码。这项工作对于发展对预测编码和大脑中的奖励处理的综合理解是必不可少的。我们的发现将进一步发展生物学上可信的学习算法，并确定在衰老过程中维护和恢复奖励处理系统的目标。

Making and evaluating predictions is a fundamental feature of brain function. All animals learn from experience to optimize their behaviour for survival, yet we don't know how key 'prediction nodes' in the brain talk to each other, or indeed what the conversation is. Consider the familiar jingle of an ice cream van; our brains have learnt through experience that this sound signals the availability of ice cream and how good the treat will be, guiding our decision to approach, or avoid it. The cerebellum and ventral tegmental area (VTA) are critical hubs for prediction, but until now have been considered as independent systems. New evidence reveals prominent connections from the cerebellum to the VTA that can influence reward-seeking behaviour and thus 1) redefine our view of the function of these systems and 2) indicate that predictions may be coordinated throughout the brain. The goal of this proposal is to provide a detailed circuit-level characterisation of the role of the cerebellum in the encoding of reward prediction error in the VTA. To achieve this, we will first map the organisation of cerebellar input onto dopaminergic and non-dopaminergic neurons within the VTA. Second, we will define the nature of the cerebellar signal to the VTA by recording from projection neurons during reward prediction behaviour. Third, we will selectively disrupt these inputs during behaviour to establish how the cerebellum shapes reward prediction error coding in the VTA. This work is essential to develop an integrated understanding of predictive coding and reward processing in the brain. Our findings will further the development of biologically plausible learning algorithms, and identification of targets for the maintenance and restoration of reward processing systems during ageing.