Uncovering the neural basis of movement transitions

揭示运动转换的神经基础

基本信息

  • 批准号:
    MR/S025944/1
  • 负责人:
  • 金额:
    $ 39.5万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2020
  • 资助国家:
    英国
  • 起止时间:
    2020 至 无数据
  • 项目状态:
    已结题

项目摘要

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.
你觉得口渴了。你的手臂向外移动,伸手去拿办公桌上的咖啡,抓住把手,然后平稳地将杯子移回等待的嘴唇。你正走到公共汽车站,去参加一个重要的面试。当公交车出人意料地在你前面转弯时,你的双腿摇摆,然后左转,然后右转,然后左转然后右转。惊慌失措的你开始奔跑,双腿抽动,双脚每迈一步都离开地面。这两者都是动作之间的过渡,都是手臂伸出来、停下来、再回来的离散动作之间的过渡,也是从走路到跑步的有节奏的动作之间的过渡。但是,尽管我们知道大脑如何代表和控制单个动作,但我们对大脑如何控制它们之间的转换知之甚少。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,不同的动作是由大脑中同一组神经元控制的。在你的运动皮质中有一组神经元控制着手臂的运动。在其他地方,有一组神经元创造了腿部运动的节奏。不知何故,这些神经元的活动从代表一种运动变化到另一种运动,并且变化得很顺利,这样你就不会僵住在原地。因此,我们提出的工作旨在解决一个有趣的问题,即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,但又能平稳地进行。为了解决离散运动的这个问题,我们将研究猴子运动皮质中的神经活动,这些猴子通过移动手臂来控制操纵杆。猴子的目标是移动操纵杆,连续击中四个目标中的每一个,目标之间的每一次移动都会产生离散的手臂运动。为了解决这个有节奏的运动问题,我们将研究海懒从静止到奔跑再到正常爬行的爬行回路中的神经活动。研究海懒的节律转换具有独特的优势,我们可以在实验室可靠地引发这种逃逸反应,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的转换的深层问题。第一个是找出神经活动的哪种模式创造了哪种运动。我们将开发方法来找出这些模式何时以及如何变化,并将这些变化与猴子和海参的运动进行比较。这将揭示运动中转换的基本神经“代码”。第二个是了解单个神经元是否对转换很重要。比如,负责奔驰的活动模式在一组神经元之间是相同的;这些神经元的不同组合可以创造出大致相同的模式。因此,可能只有模式始终如一地被创造出来,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,发现电路的哪些物理变化会导致活动模式的变化。我们将使用电路模型来区分运动之间的时间和类型的变化是由电路输入的变化、神经元之间的线路变化还是其他什么引起的。这些洞察力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。通过揭示大脑如何在运动之间成功而平稳地移动身体,我们的结果可以为患者控制假肢或机械臂提供丰富的新选择,以及为机器人的运动设计控制器。

项目成果

期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
The Spike: An Epic Journey Through the Brain in 2.1 Seconds
《The Spike》:2.1 秒内的史诗般的大脑之旅
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Humphries Mark
  • 通讯作者:
    Humphries Mark
Activity Subspaces in Medial Prefrontal Cortex Distinguish States of the World.
Bayesian Mapping of the Striatal Microcircuit Reveals Robust Asymmetries in the Probabilities and Distances of Connections.
纹状体微电路的贝叶斯映射揭示了连接概率和距离的鲁棒不对称性。
Bayesian mapping of the striatal microcircuit reveals robust asymmetries in the probabilities and distances of connections
纹状体微电路的贝叶斯映射揭示了连接概率和距离的鲁棒不对称性
  • DOI:
    10.1101/2021.06.08.447507
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Cinotti F
  • 通讯作者:
    Cinotti F
Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models.
  • DOI:
    10.1371/journal.pone.0254057
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Humphries MD;Caballero JA;Evans M;Maggi S;Singh A
  • 通讯作者:
    Singh A
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Mark Humphries其他文献

Real time systems laboratory development: Experiments focusing on a dual core processor
实时系统实验室开发:专注于双核处理器的实验
  • DOI:
    10.18260/1-2--451
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    0
  • 作者:
    M. Shirvaikar;Mark Humphries;L. Estevez
  • 通讯作者:
    L. Estevez

Mark Humphries的其他文献

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{{ truncateString('Mark Humphries', 18)}}的其他基金

The computational basis of foraging
觅食的计算基础
  • 批准号:
    BB/X013111/1
  • 财政年份:
    2023
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Research Grant
Networks of neural dynamics: Knowledge-discovery for experimental neuroscience
神经动力学网络:实验神经科学的知识发现
  • 批准号:
    MR/J008648/2
  • 财政年份:
    2018
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Fellowship
Resolving the size and nature of neocortical population codes
解决新皮质群体代码的大小和性质
  • 批准号:
    MR/P005659/2
  • 财政年份:
    2018
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Research Grant
Resolving the size and nature of neocortical population codes
解决新皮质群体代码的大小和性质
  • 批准号:
    MR/P005659/1
  • 财政年份:
    2017
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Research Grant
Networks of neural dynamics: Knowledge-discovery for experimental neuroscience
神经动力学网络:实验神经科学的知识发现
  • 批准号:
    MR/J008648/1
  • 财政年份:
    2012
  • 资助金额:
    $ 39.5万
  • 项目类别:
    Fellowship

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