Uncovering the neural basis of movement transitions

你渴了。你的手臂向外移动，伸手去拿桌子上的咖啡杯，抓住把手，把杯子平稳地移回你等待的嘴唇。你正走向公交车站，去参加一个重要的面试。当公交车意外地在你前面转弯时，腿摆动，左然后右，左然后右。惊慌失措的你开始奔跑，双腿不停地蹬着，每跨一步，脚都会离开地板。两者都是运动之间的过渡，手臂的离散运动之间的过渡--伸出，然后停下来，然后回来--以及步行到跑步的节奏运动之间的过渡。但是，尽管我们对大脑如何代表和控制单个动作了解很多，但我们对它如何控制它们之间的过渡知之甚少。了解这一点将有助于我们为瘫痪和残疾人制造更好的智能假肢;并为移动机器人制造更好，更自然的控制器。挑战在于，不同的运动是由大脑中的同一组神经元控制的。在你的运动皮层中有一组神经元控制手臂的运动。在其他地方，有一组神经元产生腿部运动的节奏。不知何故，这些神经元的活动从代表一种运动转变为另一种运动，而且变化得如此平稳，以至于你不会呆在原地。因此，我们提出的工作旨在解决一个有趣的问题：一组神经元如何在不同的活动模式之间变化，以至于它们各自产生不同的运动，但又如此平稳。为了解决离散运动的问题，我们将研究猴子移动手臂控制操纵杆时运动皮层的神经活动。猴子的目标是移动操纵杆，连续击中四个目标中的每一个，目标之间的每一个移动都是一个离散的手臂运动。为了解决这个问题的节奏运动，我们将研究神经活动的爬行回路海蛞蝓逃脱，改变从静止，疾驰，爬行正常。研究海蛞蝓的节律转换具有独特的优势，我们可以在实验室中可靠地引起这种逃避反应，同时可以记录大约10%的基本神经元的每一次输出。这些数据将让我们回答一些关于大脑如何控制运动之间转换的深层次问题。第一个是找出哪种神经活动模式产生了哪种运动。我们将开发方法来发现模式何时以及如何变化，并将这些变化与猴子和海蛞蝓的运动进行比较。这将揭示运动转换的基本神经“代码”。第二是了解单个神经元对转换是否重要。比如说，一组神经元共享了导致飞奔的活动模式;这些神经元的不同组合可以产生大致相同的模式。因此，可能只有模式是一致创建的，而不是单个神经元的活动。了解这一点将有助于我们更好地理解如何从大脑活动中解码运动。第三是发现回路的哪些物理变化导致了活动模式的变化。我们将使用电路模型来区分动作之间的变化的时间和类型是由电路输入的变化引起的，还是神经元之间的布线变化引起的，还是其他原因。这些见解将帮助我们设计更好的方法，通过控制大脑活动的变化来控制运动之间的变化。通过揭示大脑如何成功和顺利地在运动之间移动身体，我们的研究结果可以为患者控制人造或机器人肢体提供丰富的新选择，并为机器人设计运动控制器。

You're feeling thirsty. Your arm moves outwards, reaching for the cup of coffee on your desk, grasps the handle, and moves the cup smoothly back to your waiting lips. You're walking to the bus stop, on your way to an important interview. Legs swinging, left then right, left then right, when the bus unexpectedly turns the corner ahead of you. Panicked, you break into a run, legs pumping, feet leaving the floor with each stride. Both are transitions between movements, between the discrete movements of the arm - out, then stop, then back - and between the rhythmic movements of walking to running. But while we know much about how the brain represents and controls single movements, we know little about how it controls the transitions between them. Understanding this would help us build better intelligent prosthetics for the paralysed and disabled; and build better, more natural controllers for moving robots. The challenge is that different movements are controlled by the same set of neurons in the brain. There are a set of neurons in your motor cortex that control arm movement. Elsewhere there are a set of neurons that create the rhythms of leg movement. Somehow, the activity of those same neurons changes from representing one movement to another, and does so smoothly, so that you do not freeze in place.Our proposed work thus aims to tackle the intriguing problem of how a single group of neurons changes between patterns of activity so different that they each generate different movements, yet does so smoothly. To tackle this problem for discrete movements, we will study neural activity in the motor cortex of monkeys moving their arms to control a joystick. The monkey's goal is to move the joystick to hit each of four targets in a row, each movement between targets thus creating a discrete arm movement. To tackle this problem for rhythmic movements, we will study neural activity in the crawling circuit of sea-slugs escaping, changing from being still, to galloping, to crawling normally. Studying rhythmic transitions in sea-slugs has the unique advantages that we can reliably cause this escape response in the lab, and at the same time can record every output from about ten percent of all the essential neurons.These data will let us answer some deep questions about how brains control transitions between movements. The first is to work out which pattern of neural activity creates which movement. We will develop methods to find when and how the patterns change, and compare these changes to the movements in monkeys and sea-slugs. This will reveal the basic neural "code" for transitions in movements.The second is to understand if single neurons are important for transitions. The pattern of activity that is responsible for, say, galloping is shared among a set of neurons; and approximately the same pattern can be created by different combinations of those neurons. So it may be that only the pattern is consistently created, and not the activity of individual neurons. Knowing this will help us better understand how to decode movements from brain activity.The third is to discover what physical changes to the circuit create the changes in activity pattern. We will use models of circuits to pick apart whether the timing and type of changes between movements are caused by changes to the inputs to the circuit, changes to the wiring between neurons, or something else. These insights this will help us design better ways to control changes between movements by controlling changes in brain activity.By revealing how brains successfully and smoothly move bodies between movements, our results could provide a wealth of new options for the control of artificial or robotic limbs by patients, and for designing controllers for movement in robots.