Extracting computational principles governing the relation between brain activity and muscle activity that are conserved between rodents and primates

提取啮齿类动物和灵长类动物之间保守的大脑活动和肌肉活动之间关系的计算原理

• 批准号: 9983208
• 负责人: Mark Montgomery Churchland
• 金额: $ 37.65万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-25 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9983208
• 关键词: Adopted; Area; Biological; Brain; Complex; Data; Dimensions; Evolution; Extensor; Flexor; Foundations; Goals; Interneurons; Lead; Link; Literature; Logic; Machine Learning; Measures; Methods; Motor; Motor Cortex; Motor Neurons; Movement; Muscle; Nature; Neurons; Noise; Pattern; Population; Population Dynamics; Primates; Property; Recurrence; Rodent; Role; Smooth Muscle; Somatosensory Cortex; Spinal; Structure; Surface; System; Techniques; Testing; Training; Undifferentiated; analytical method; base; computer framework; motor control; muscular system; network architecture; network models; novel; predictive modeling; relating to nervous system; response; theories

Abstract Since the late 1960’s, a large literature has attempted to characterize the properties of neural responses in motor areas of the brain, and to relate those responses to externally measured variables such as muscle activity or reach direction. In some ways this field has been very successful: early studies revealed robust movement-related modulation of neural firing rates, across a broad network of reciprocally connected areas. Such activity is broadly tuned, in the sense that most neurons respond during most movements, and it was thus appreciated that the relevant computations must be understood at the population level, rather than via the properties of a small subset of responsive neurons. Yet the nature of that population-level computation has remained controversial. This is true even of primary motor cortex, which has made it harder still to characterize and contrast the different computations made by different cortical areas. In general, there remains vigorous disagreement regarding the relationship of cortical activity to the ultimate of the motor system: complex, intricate, temporally rich patterns of activity across a large population of muscles. A fundamental conundrum has been that neural responses in motor cortex (and elsewhere) resemble the responses of muscles in some ways but not others. We will attempt to resolve this apparent paradox through two means. First, we will use emerging methods in rodent that allow recordings from subpopulations of motor cortex neurons, identified via the populations of spinal interneurons to which they project. This will allow us to ask whether the logic of motor cortex responses becomes clearer when subpopulations, with potentially very different roles, are segregated rather than lumped together. Second, we will use analysis methods motivated by network-theory to characterize computationally relevant aspects of the population response. Such methods, many of which exploit machine-learning techniques, hold the promise of explaining otherwise confusing aspects of the population response. Such methods can seek structure predicted by models, determine if it is present, and if so whether it is differentially present across different populations (i.e., subpopulations within motor cortex and populations in other cortical areas). We will also use network modeling both to produce hypotheses, and to explore the computational relevance of novel structure uncovered by our methods. Preliminary data indicate that different populations can appear very similar when analyzed via traditional means, yet show very different population-level structure when approached via our novel methods. Our believe is that a combination of network modeling, analyses inspired by computational-level theories, and a variety of novel classes of data, will allow progress in defining what motor cortex shares with the downstream muscles, what additional computationally relevant properties motor cortex has that the muscles do not, how various computationally relevant properties are / aren’t shared among motor cortex subpopulations, and how the population response in different cortical areas varies in computationally relevant dimensions.

抽象的 自 20 世纪 60 年代末以来，大量文献试图描述神经反应的特性 大脑的运动区域，并将这些反应与外部测量的变量（例如肌肉）联系起来 活动或到达方向。在某些方面，这个领域非常成功：早期研究表明， 跨相互连接区域的广泛网络，对神经放电率进行与运动相关的调节。 这种活动是广泛调整的，从某种意义上说，大多数神经元在大多数运动中都会做出反应，而且 因此认识到相关计算必须在人口层面上理解，而不是通过 一小部分响应神经元的特性。然而，人口层面计算的性质已经 仍然存在争议。即使对于初级运动皮层也是如此，这使得表征更加困难 并对比不同皮质区域所做的不同计算。总体而言，依然充满活力 关于皮质活动与运动系统终极关系的分歧：复杂， 大量肌肉中错综复杂、时间丰富的活动模式。一个基本难题 运动皮层（和其他地方）的神经反应类似于某些肌肉的反应 方式，但不是其他方式。我们将尝试通过两种方法来解决这个明显的悖论。首先，我们将使用 在啮齿动物中出现的新兴方法可以记录运动皮层神经元亚群的信息，这些方法通过 它们投射到的脊髓中间神经元群。这将使我们能够问电机的逻辑是否 当具有可能非常不同的角色的亚群被隔离时，皮层反应变得更加清晰 而不是混在一起。其次，我们将使用受网络理论启发的分析方法 表征总体响应的计算相关方面。诸如此类的方法，很多 利用机器学习技术，有望解释其他令人困惑的方面 人口反应。这些方法可以寻找模型预测的结构，确定它是否存在，以及是否存在 那么它是否在不同人群（即运动皮层内的亚群和 其他皮质区域的人群）。我们还将使用网络建模来产生假设，并 探索我们的方法发现的新颖结构的计算相关性。初步数据表明 当通过传统方法分析时，不同的人群可能看起来非常相似，但又表现出非常不同 通过我们的新颖方法来研究人口水平结构。我们相信，结合 网络建模、受计算级理论启发的分析以及各种新颖的数据类别， 将在定义运动皮层与下游肌肉共享的内容、额外的内容方面取得进展 运动皮层具有肌肉所没有的计算相关特性，计算上有何不同 相关属性在运动皮层亚群之间共享/不共享，以及群体如何反应 不同皮质区域的计算相关维度有所不同。