Resolving the size and nature of neocortical population codes

皮层是我们最基本和最先进的大脑功能的来源，包括我们如何听、看和触摸；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单个神经元的联合活动产生的。在这些庞大的数量中，一小部分神经元有特定的作用。一组可能会发出高音；一个人可能会对着食指尖上的布刷发火；另一个动作是开始移动你的右肘。我们的提案提出了一个简单的问题：为了完成一项任务，大脑使用了多少组集合，扮演了多少角色？想象一下，完成一项特定任务所必需的大脑部分是一个在隔音房间里演奏的管弦乐队。我们的问题就像在问：我们如何知道他们在玩什么游戏？然后算出每组乐器在谱子中的角色？到目前为止，我们的大脑记录技术就像盲目地把麦克风随机地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单个乐器参与其中，更不用说他们如何相互作用，或分组到他们的木材，弦乐器和其他合奏。我们甚至不知道管弦乐队有多大。因此，为了解决重建大脑任务得分的问题，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。然后我们可以从这些杂音中找出它们代表的合奏和乐器，它们的角色，以及它们如何结合起来创造出完整的分数。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。这是由于老鼠脸上的胡须和它们在大脑中的表现方式之间奇妙而整齐的对应关系。老鼠可以通过用一根须接触一根杆子来学会发现两个喷水口中哪一个有水。这种单一的须状物在大脑皮层由一个桶状的神经元柱表示。它足够小，一个实验室现在已经记录了老鼠试图取水时上半部分每个神经元的活动。作为单个须的唯一表示，它必须包含小鼠解决任务所需的所有信息。所以我们知道这些数据中必须包含大脑的管弦乐队来完成这项任务。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，扮演多少角色，来完成这项任务？为此，我们将使用所谓的“无监督”方法，这种算法可以自行确定数据中有多少不同的神经元集，它们有多大，以及哪些神经元属于哪个集。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。找到这些组合后，我们就可以通过将它们的活动与老鼠的行为进行比较，找出它们的作用：例如，我们可以计算出某些组合在老鼠移动胡须或舔水时是否处于活动状态。如果我们回答了这个问题，我们学到了什么？我们将学习大脑皮层如何进行计算的基本组成部分。如果我们只能在N组神经元中表示N个事物，那么我们的思考能力就有了上限。我们将了解大脑皮层对损伤的恢复能力，无论是意外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，并像往常一样继续工作。但是，如果某些设置具有独特的作用，那么对它们的损害，无论多么小，都可能是灾难性的。最后，我们将了解这些集合是如何结合起来产生满分的。实验室和诊所正在探索如何传输运动皮层的小块活动，让病人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，那么这种控制的准确性将提高许多倍。

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.