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Resolving the size and nature of neocortical population codes

解决新皮质群体代码的大小和性质

基本信息

批准号：
MR/P005659/1
负责人：
Mark Humphries
金额：
$ 32.54万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FP005659%2F1
关键词：
Resolving size nature neocortical population

项目摘要

Cortex is the source of our most basic and most advanced brain functions, of how we hear, see, and touch; of how we think, plan, and act. All arise from the combined activity of millions or billions of individual neurons. Within these gargantuan numbers, small sets of neurons have specific roles. One set might fire to a high pitched tone; one might fire to the brush of cloth on the tip of an index finger; yet another to start moving your right elbow. Our proposal asks the simple question: to do a task, how many sets, with how many roles, does the brain use? Imagine the part of the brain necessary for doing a particular task is an orchestra playing in a sound proofed room. Our question is the same as asking: how can we work out what score they are playing? And work out the roles of each set of instruments within that score? Up till now, our brain recording technology has been like blindly lowering microphones at random next to one or two players in the orchestra, listening for a few minutes, then trying to reconstruct the entire score. Done this way, we have no idea of which type of individual instruments are involved, let alone how they interact, or group into their wood, string and other ensembles. We don't even know how big the orchestra is. So to solve the problem of reconstructing the brain's score for a task, we need to be able to record the whole orchestra of neurons at once, one microphone per neuron. We can then work out from that cacophony what ensembles and instruments they represent, their roles, and how they combine to create the full score. Recent technological advances means that we now have the right kind of one-microphone-per-neuron data. This has been made possible by the wonderfully neat correspondence between the whiskers on a mouse's face and the way a whisker is represented in the brain. Mice can learn to find which of two spouts contains water by touching a pole with a single whisker. This single whisker is represented in their cortex by a barrel-shaped column of neurons. It is small enough that a lab has now recorded the activity of every neuron in its top half while the mice tried to get their water. As the only representation of that single whisker, it must contain all the information the mice need to solve the task. So we know these data must contain within them the brain's orchestra for this task. Our goal is to use this data to answer our question: how many sets of neurons, with how many roles, does the brain need to solve this task?To do so, we will use so-called "unsupervised" methods, algorithms that can determine for themselves how many different sets of neurons there are in the data, how large they are, and which neurons belong to which sets. They do this by working out which neurons are consistently active at the same time. Having found the sets, we can then find out the their roles by comparing their activity with the mouse's behaviour: for example, we can work out if some sets are active while it moves its whiskers, or while it licks the water.If we answer this question, what do we learn? We will learn about the basic building blocks of how cortex computes. If we can only represent N things in N sets of neurons, then that places an upper limit on our capacity to think. We will learn about the resilience of cortex to damage, whether through accidents or diseases such as dementia. If multiple sets of neurons have the same task, then we may lose some and carry on as normal. But if some sets have a unique role, then damage to them, however small, could be disastrous. Ultimately, we will learn about how these sets combine to produce the full score. Labs and clinics are exploring how we can transmit the activity of small bits of motor cortex to give patients direct control over their artificial limbs. If we knew how to work out the full score for controlling limb movement, the accuracy of this control would improve many times over.

皮质是我们最基本和最高级大脑功能的来源，这些功能决定了我们如何听、看和触摸，以及我们如何思考、计划和行动。所有这些都是由数百万或数十亿个神经元的联合活动产生的。在这些庞大的数字中，小部分神经元具有特定的作用。一组可能会发射到高音；一组可能会发射到食指尖端的布刷上；还有一组可能会开始移动你的右肘。我们的提案问了一个简单的问题：要完成一项任务，大脑使用多少组，多少个角色？想象一下，完成一项特定任务所必需的大脑部分是在隔音房间里演奏的管弦乐队。我们的问题等同于问：我们如何才能计算出他们的比分是多少？并计算出每套乐器在该乐谱中的角色？到目前为止，我们的大脑录音技术就像是盲目地将麦克风放在管弦乐队中一两个演奏者旁边，听上几分钟，然后试图重建整个乐谱。这样做，我们不知道涉及哪种类型的单个乐器，更不用说它们如何相互作用，或者如何组合成他们的木质、弦乐和其他合奏。我们甚至不知道管弦乐队有多大。因此，要解决为一项任务重建大脑分数的问题，我们需要能够一次记录整个神经元管弦乐队，每个神经元一个麦克风。然后，我们可以从这些刺耳的声音中找出他们代表的乐团和乐器，他们的角色，以及他们如何结合在一起创造出完整的乐谱。最近的技术进步意味着我们现在拥有了正确的每个神经元一个麦克风的数据。这是由于老鼠脸上的胡须和大脑中胡须的表现方式之间的巧妙对应而成为可能。老鼠可以通过用一根胡须触摸一根柱子来学习找出两个喷嘴中哪一个含有水。这种单一的胡须在它们的大脑皮层中由一个桶形的神经元柱表示。它足够小，以至于实验室现在已经记录了小鼠试图取水时其上半部分每一个神经元的活动。作为单一胡须的唯一代表，它必须包含老鼠解决任务所需的所有信息。因此，我们知道这些数据中一定包含了完成这项任务的大脑管弦乐队。我们的目标是使用这些数据来回答我们的问题：大脑需要多少组神经元和多少个角色来解决这项任务？为此，我们将使用所谓的“无监督”方法，这些算法可以自己确定数据中有多少不同的神经元集合，它们有多大，以及哪些神经元属于哪些集合。他们通过计算出哪些神经元在同一时间持续活跃来做到这一点。找到集合后，我们就可以通过比较它们的活动和老鼠的行为来找出它们的作用：例如，我们可以计算出一些集合在它移动胡须或舔水时是否处于活动状态。如果我们回答这个问题，我们会学到什么？我们将学习皮质如何计算的基本构件。如果我们只能在N组神经元中代表N个事物，那么我们的思考能力就有了一个上限。我们将学习大脑皮层对损伤的弹性，无论是通过事故还是通过痴呆症等疾病。如果多组神经元有相同的任务，那么我们可能会失去一些，并照常进行。但如果有些布景具有独特的作用，那么它们受到的损害，无论多么微小，都可能是灾难性的。最终，我们将了解这些集合是如何组合在一起产生满分的。实验室和诊所正在探索如何将小块运动皮质的活动传递给患者，让患者直接控制他们的假肢。如果我们知道如何计算出控制肢体运动的满分，这种控制的准确性将提高许多倍。