Prediction mechanisms of the brain: a computational taxonomy

我们的大脑从感官接收到连续不断的未经分类的嘈杂数据——光的变化模式，声音频率的分布，以及来自我们皮肤和肌肉的关于身体位置和运动的信号。然而，我们感知到的是一个有序而有意义的物体、语言和音乐世界（而不是光和声音能量的模式），在这个世界中，我们产生了指向行为目标的行动（而不是输出肢体位置和肌肉紧张的混乱）。为了从混乱中创造秩序，大脑不断地构建和完善感官世界的模型，以及动物或人在其中的行为。这些模型通过捕捉外部世界中有意义的结构而忽略非结构化、无意义或不相关的信息来简化信息处理。从这个意义上说，我们可以把大脑想象成科学家，通过简化到核心元素，积极地试图理解和预测周围的世界。在我的研究中，我试图回答一些相当普遍的问题，即大脑是如何构建内部模型来准确捕捉外部世界的结构的。我将针对大脑在不同情况下反复面临的三个信息处理挑战，并试图弄清楚大脑是否有专门的系统来解决这些问题，如果有，这些系统是如何工作的：大脑如何专注于刺激的相关变化（如汽车接近人行横道的速度），并过滤掉无关的变化（如汽车的颜色）？-大脑如何优化相关刺激的处理（例如，将注意力集中在具有非常不同含义的相似刺激上，因此需要仔细处理）？-随着环境的变化，大脑如何决定对世界的期望改变多少？如果我经历了一个意外的事件（比如上班路上的交通增加了），我应该在多大程度上更新我对未来事件的期望？我是否可以自愿地让我的大脑学习得更快（例如，如果我知道正在进行施工，预计交通流量会发生变化），然后再次忘记（当施工结束时）？我理解大脑如何应对这些挑战的方法是开发算法（在计算机程序中实现），这些算法在解决这些挑战时的行为方式与人类大脑相似，然后研究如何通过真实大脑中的神经元群来计算这些算法。然后，为了测试算法是否正确（也就是说，大脑是否真的以这种方式工作），我使用我的神经网络模型来生成新的预测，预测人们在实验中的行为，以及他们的大脑活动在不同情况下会如何变化。我使用非侵入性成像技术如核磁共振成像来测量大脑活动。我的一些模型还预测了不同的大脑化学物质（神经递质和神经调节剂）如何影响大脑对信息的处理。为了验证这些假设，我将给健康的志愿者服用小剂量的影响神经递质浓度的药物，并观察他们的行为和大脑活动的变化。

Our brains receive a continuous torrent of unsorted, noisy data from the senses - changing patterns of light, distributions of sound frequencies, and signals from our skin and muscles about the position and movements of the body. Yet we perceive an ordered and meaningful world of objects, speech and music (not patterns of light and sound energy), in which we produce actions directed towards behavioural goals (rather than outputting jumble of limb locations and muscle tensions). To create order from chaos, the brain is continually constructing and refining models of the sensory world and the animal's or person's actions in it. These models simplify information processing by capturing the meaningful structure in the external world and ignoring information that is unstructured, meaningless or irrelevant. In this sense we can think of brains almost like scientists, actively trying to understand and predict the world around them by simplifying it down to its core elements. In my research, I am trying to answer some rather general questions about how the brain constructs internal models that accurately capture the structure of the external world. I will be targeting three information processing challenges that brains face again and again in different contexts, and trying to work out if the brain has dedicated systems for solving these problems, and if so, how those systems work: - How does the brain focus on relevant variation in stimuli (such as the rate at which cars are approaching a pedestrian crossing) and filter out irrelevant variation (such as the colour of the cars)? - How does the brain optimise processing of relevant stimuli (for example, focussing attention on stimuli where similar stimuli have very different meanings, and so careful processing is required?- How does the brain determine how much to change its expectations about the world, as the environment changes? If I experience a surprising event (such as an increase in traffic on my route to work), how much should I update my expectations for future occasions? Can I voluntarily set my brain to learn faster (for example, if I expect a change in traffic flow because I know that construction work is taking place) and to forget again (when the construction work is over)?My approach to understanding how the brain meets these challenges is to develop algorithms (implemented in computer programmes) that behave in a similar way to human brains in solving these challenges, and then to work out how these algorithms could be computed by populations of neurons in a real brain. Then to test whether the algorithms are correct (that is, whether the brain really works that way), I use my neural network models to generate new predictions about how people will behave in experiments, and how their brain activity will change in different circumstances. I measure brain activity using non-invasive imaging techniques such as MRI. Some of my models also make predictions about how different brain chemicals (neurotransmitters and neuromodulators) affect the processing of information by the brain. To test these hypotheses, I will give healthy volunteers small doses of drugs that affect neurotransmitter concentrations and observe the resulting changes in their behaviour and brain activity.