权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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/P005659/2
负责人：
Mark Humphries
金额：
$ 19.67万
依托单位：
University of Nottingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FP005659%2F2
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.

皮层是我们最基本和最高级的大脑功能的来源，我们如何听到，看到，触摸;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。在这些庞大的数量中，小的神经元集合具有特定的作用。一组可能会发射到一个高音调的音调;一组可能会发射到食指尖上的布刷;还有一组开始移动你的右肘。我们的建议提出了一个简单的问题：做一项任务，大脑使用了多少组，有多少个角色？想象一下，大脑中完成一项特定任务所必需的部分是一个在隔音房间里演奏的管弦乐队。我们的问题就像是在问：我们如何才能算出他们打的是什么分数？找出每一套乐器在乐谱中的作用？到目前为止，我们的大脑记录技术就像盲目地将麦克风随机放在管弦乐队的一两个演奏者旁边，听几分钟，然后试图重建整个乐谱。这样做，我们不知道哪种类型的个别文书涉及，更不用说他们如何相互作用，或组成他们的木材，字符串和其他合奏。我们甚至不知道管弦乐队有多大。因此，要解决重建大脑对一项任务的评分的问题，我们需要能够一次记录整个神经元乐队，每个神经元一个麦克风。然后，我们可以从这种不和谐的声音中弄清楚他们代表什么样的合奏和乐器，他们的角色，以及他们如何联合收割机组合创造出完整的乐谱。最近的技术进步意味着我们现在有了正确的每个神经元一个麦克风的数据。这是因为老鼠脸上的胡须与胡须在大脑中的表现方式之间有着惊人的一致性。老鼠可以通过用一根触须触摸一根杆子来学习找出两个喷口中哪一个有水。这一根触须在它们的皮层中由一个桶形的神经元柱代表。它足够小，以至于一个实验室现在已经记录了老鼠试图喝水时上半部分每个神经元的活动。作为这一根触须的唯一代表，它必须包含老鼠完成任务所需的所有信息。所以我们知道这些数据中一定包含了大脑执行这项任务的管弦乐队。我们的目标是用这些数据来回答我们的问题：大脑需要多少组神经元，有多少个角色来解决这个任务？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。他们通过找出哪些神经元在同一时间持续活跃来做到这一点。找到这些集合后，我们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，我们可以计算出当它移动胡须或舔水时，某些集合是否是活跃的。如果我们回答这个问题，我们会学到什么？我们将学习皮层如何计算的基本构建块。如果我们只能用N组神经元来表示N件事情，那么这就限制了我们思考的能力。我们将了解皮层对损伤的恢复能力，无论是通过事故还是痴呆症等疾病。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。但是，如果某些集合具有独特的作用，那么对它们的损害，无论多么小，都可能是灾难性的。最终，我们将了解这些集合如何结合联合收割机来产生满分。实验室和诊所都在探索如何将运动皮层的活动传递给病人，让他们直接控制假肢。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。