How do neuromodulators encode uncertainty to aid flexible behaviour?

在一个充满不确定性的不断变化的世界里，我们如何快速做出决定？健康的人类在日常生活中非常善于做出最佳选择;也许决定公共汽车是最快和最好的上班方式。尽管公交车的确切行程时间有一些不确定性，但如果它是最快的，我们应该坚持我们的选择。但是，如果巴士路线突然修路，我们可能要灵活适应，改为步行到更远的火车站，以获得最快的旅程。我们可以想象，如果我们不能很好地适应不确定性，我们最终会做出错误的选择。在一种情况下，我们可能每天都换交通工具，只是因为公共汽车晚了一次。或者，当道路工程发生时，我们可能无法适应，仍然喜欢呆在慢巴士上。这两种情况都会导致更长的旅行时间，从而导致更糟糕的选择。事实证明，在不确定的情况下做出好的选择所需的计算并不简单--无论是计算机还是机器人，还是患有精神疾病的人，都不擅长灵活的决策。目前，机器学习算法和机器人在执行单一的、定义好的任务方面表现得非常出色;但大多数算法和机器人在适应新环境或执行许多不同的任务方面都失败了。因此，了解大脑中的哪些过程可以帮助我们做出灵活的选择，这可能对人工智能和机器人技术的发展以及心理健康都有好处。 到目前为止，科学家们认为，做出适应性选择，我们的大脑形成了对世界的“最佳猜测”（预测），以及什么行动可能会在未来带来回报，这对我们有帮助。然而，我们并不知道具体哪些生物信号在哪些区域可能携带着关于我们不确定世界的这些“预测”的信息。在这个项目中，我将研究两种特定的神经化学物质（称为去甲肾上腺素和乙酰胆碱），以询问它们是否可能作为潜在的生物“不确定性信号”。这些神经化学物质将有一个荧光标记，允许我用光度显微镜和插入活老鼠大脑的光纤来监测它们;所有这些都是在动物解决一个不确定的决策游戏时进行的。 我还将在已知与学习和决策有关的特定大脑区域（称为海马和前额叶皮层）使用药物来操纵这些神经化学物质。我们需要这些区域的神经递质来对我们的环境做出正确的猜测并预测正确的选择吗？研究这些问题可以更好地指导未来的精神病药物（如改进的抗抑郁药）的药物靶点。在研究了相关的神经化学物质和大脑灵活的决策之后，我的目标是建立计算模型来测试大脑可能使用的不同潜在机制，并看看哪种模型最适合动物的选择。这些成功的模型可以为未来灵活的多任务机器学习算法提供信息，这些算法与广泛的领域相关;从技术和工程行业到金融，再到医疗保健。

How do we decide things quickly in an everchanging world with a lot of uncertainty? Healthy humans are exceptionally good at making optimal choices in everyday life; perhaps deciding that the bus is the fastest and best way to get to work. Despite small uncertainties in the exact journey time of the bus, we should stick to our choice if it's the fastest. However, if there is a sudden roadwork on the bus route, we may have to adapt flexibly and instead walk to the train station further away to get the quickest journey. We can imagine that if we cannot adapt well to uncertainty, we end up with bad choices. In one scenario, we may change transportation every day just because the bus was late once. Alternatively, when the road work happen, we may fail to adapt and still favor staying on the slow bus. Both of these scenarios result in a worse choice in the form of longer travel time. It turns out that computations needed for making good choices under uncertainty are not simple - neither computers or robots, nor people with psychiatric disorders are particularly good at flexible decision making. Currently, machine learning algorithms and robots can be amazingly good at performing single, defined tasks; but most fail at adapting to new environments or performing many different tasks. Hence, it would be useful to understand what processes in the brain help us make flexible choices, possibly benefiting both the advancing field of artificial intelligence and robotics, as well as mental health care. So far, scientist believe that making adaptive choices, our brain forms 'best guesses' (predictions) about the world and what actions might lead to something rewarding in the future helps us. However, we don't know in detail what biological signals in what area may carry information on these 'predictions' about our uncertain world. In this project, I will look at two specific neurochemicals (called noradrenaline and acetylcholine), to ask if these may be acting as potential biological 'uncertainty signals'. These neurochemicals will have a fluorescent tag, allowing me to monitor them with a photometry microscope and optical fibers inserted into the brains of living mice; all whilst animals are solving an uncertain decision-making game. I will also manipulate these neurochemicals with drugs in specific brain areas know to be involved in learning and decision making (called Hippocampus and Prefrontal Cortex). Do we need the neurotransmitters in these areas to make good guesses about our environment and predict good choices? Answering these questions could better guide what drug targets are relevant for future psychiatric drugs, such as improved antidepressants. After having investigated the relevant neurochemicals and brain flexible decision making, I aim to build computational models testing different potential mechanism that the brain might be using, and see which model best fits the animal's choices. Such successful models could inform future flexible, multi-task machine learning algorithms, relevant to a broad range of fields; from tech and engineering industry, to finance, to healthcare.