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: 10224733
• 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 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10224733
• 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.

摘要 自20世纪60年代后期以来，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（如肌肉）联系起来 活动或到达方向。在某些方面，这一领域已经非常成功：早期的研究表明， 运动相关的神经放电率的调制，在一个广泛的网络连接的地区。 这样的活动是广泛调谐的，在这个意义上，大多数神经元在大多数运动中都会做出反应，而且它是 因此，人们认识到，相关的计算必须在人口水平上理解，而不是通过 一小部分反应神经元的特性。然而，人口水平计算的本质 仍然存在争议。甚至初级运动皮层也是如此，这使得它更难描述 并对比不同皮层区域的不同计算结果。总的来说， 关于皮质活动与运动系统的最终关系的分歧：复杂， 复杂的，时间丰富的活动模式在一个大的人口肌肉。一个基本的难题 运动皮层（和其他地方）的神经反应类似于肌肉的反应， 方法，而不是其他。我们将试图通过两种方法来解决这个明显的悖论。首先，我们将使用 啮齿动物中的新兴方法，允许记录运动皮层神经元的亚群，通过 它们投射到的脊髓中间神经元群体。这将使我们能够问是否电机的逻辑 当具有潜在的非常不同的角色的亚群被分离时，皮层反应变得更加清晰 而不是集中在一起。其次，我们将使用网络理论驱动的分析方法， 描述人口反应的计算相关方面。这些方法，其中许多 利用机器学习技术，保持解释的承诺，否则混乱的方面， 人口反应。这种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。 因此它是否在不同人群中差异存在（即，运动皮质内的亚群， 其他皮质区）。我们还将使用网络建模来产生假设， 探索我们的方法发现的新结构的计算相关性。初步数据显示 通过传统方法分析时，不同的人群可能看起来非常相似，但表现出非常不同的 人口水平的结构时，通过我们的新方法接近。我们认为， 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， 将允许在确定运动皮层与下游肌肉共享什么方面取得进展， 运动皮层具有肌肉所没有的计算相关特性， 相关特性在运动皮层亚群中是/不是共享的，以及群体如何响应 在不同的皮层区域中，计算相关的维度不同。