SSA: The role of VTA GABA neurons in reward learning

理解我们是如何学会在奖励结果和预测结果的线索（例如，冰淇淋叮当声和美味的款待）之间建立联系的，仍然是神经科学的核心挑战，也是解决药物成瘾等适应不良的关键。最近的证据表明，腹侧被盖区（VTA）的GABA神经元可能会影响大脑进行对学习很重要的计算的方式，但它们如何做到这一点的细节尚不清楚。此外，最近发现，这些GABA神经元有多种种群，它们在分子组成和解剖连接方面都是不同的。这就提出了一种可能性，即不同的人群在学习中扮演着不同的角色，并向不同的大脑区域传递不同的信号。本博士的重点是定义不同种群的VTA GABA神经元如何编码奖励学习的各个方面。为了实现这一目标，我们将结合尖端的神经生理学技术和先进的计算模型。为了记录行为小鼠的个体多巴胺神经元，我们将利用我们最近开发的一项新技术（Dodson et al. 2015,2016）。这种方法不仅可以确定每个记录神经元的精确位置和神经化学特性，还可以询问它们表达的关键蛋白质以及它们支配的大脑区域（从而将神经元细分为不同的种群）。然后，我们将使用计算模型来探测我们记录的信号如何与学习过程的不同部分相关。在博士期间，学生将有机会学习和发展他们的技能：体内记录，动物行为，神经解剖学和免疫组织化学，显微镜，数据分析和编程，以及计算建模

Understanding how we learn to make associations between rewarding outcomes and the cues that predict them (e.g.the ice-cream van jingle and a delicious treat) remains a central challenge for neuroscience and is critical for tacklingmaladaptations like drug addiction. Recent evidence suggests that GABA neurons in the ventral tegmental area (VTA)may shape the way the brain performs the calculations important for learning, but the details of how they do this arenot yet clear.Furthermore, it has recently been found that there are multiple populations of these GABA neurons which are diverseboth in terms of their molecular make-up and the anatomical connections they make. This raises the possibility thatdifferent populations play different roles in learning, and transmit diverse signals to different brain regions. The focusof this PhD is to define how different populations of VTA GABA neurons encode aspects of reward-learning. To achievethis, we will combine cutting-edge neurophysiological techniques and advanced computational models.To record from individual dopamine neurons in behaving mice we will take advantage of a new technique we recentlydeveloped (Dodson et al. 2015, 2016). This approach not only allows one to identify the precise location andneurochemical identity of each recorded neuron, but also to interrogate which key proteins they express and whichbrain regions they innervate (and thus subdivide neurons into their different populations). We will then usecomputational models to probe how the signals we record are related to different parts of the learning process.During the PhD, the student will have the opportunity to learn, and develop their skills in: in vivo recording, animalbehaviour, neuroanatomy and immunohistochemistry, microscopy, data analysis and programming, andcomputational modelling