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Prediction mechanisms of the brain: a computational taxonomy

大脑的预测机制：计算分类法

基本信息

批准号：
MR/L019639/1
负责人：
Jill O'Reilly
金额：
$ 122.32万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2014
资助国家：
英国
起止时间：
2014 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FL019639%2F1
关键词：
Prediction mechanisms brain computational taxonomy

项目摘要

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.

我们的大脑从感官中接收到一系列杂乱的、嘈杂的数据--光的变化模式、声音频率的分布，以及来自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由物体、言语和音乐（而不是光和声音能量的模式）组成的有序而有意义的世界，在这个世界中，我们产生了指向行为目标的行动（而不是输出一堆肢体位置和肌肉张力）。为了从混乱中创造秩序，大脑不断地构建和完善感官世界以及动物或人在其中的行为的模型。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的、无意义的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，通过将世界简化为核心元素，积极地试图理解和预测周围的世界。在我的研究中，我试图回答一些关于大脑如何构建内部模型以准确捕捉外部世界结构的相当普遍的问题。我将针对大脑在不同情况下一次又一次面临的三个信息处理挑战，并试图找出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（例如汽车接近行人过路处的速率）并过滤掉不相关的变化（例如汽车的颜色）？- 大脑如何优化相关刺激的处理（例如，将注意力集中在类似刺激具有非常不同含义的刺激上，因此需要仔细处理？）随着环境的变化，大脑如何决定改变对世界的期望？如果我经历了一个令人惊讶的事件（比如上班路上的交通流量增加），我应该对未来的情况更新多少预期？我是否可以自愿地让我的大脑更快地学习（例如，如果我知道正在进行建筑工程，所以我预计交通流量会发生变化），并再次忘记（当建筑工程结束时）？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，然后研究这些算法如何通过真实的大脑中的神经元群体进行计算。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关于人们在实验中的行为以及他们的大脑活动在不同情况下如何变化的新预测。我使用非侵入性成像技术（如MRI）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（神经递质和神经调质）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药物来影响神经递质的浓度，并观察他们的行为和大脑活动的变化。