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: 9444175
• 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
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9444175
• 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1960年代后期以来，大量文献试图表征神经反应的特性 大脑的运动区域，并将这些响应与外部测量变量（例如肌肉）联系起来 活动或达到方向。在某些方面，这个领域非常成功：早期研究表明了强大的 在广泛的相互连接区域网络上，神经导率与运动有关的调制。 从大多数运动中大多数神经元反应的意义上，这种活动是广泛调整的，这是 这感谢必须在人口层面上理解相关计算，而不是通过 一小部分反应性神经元的特性。然而，该人群级计算的性质具有 仍然有争议。即使是主电机皮层也是如此，这使得仍然很难表征 并对比不同的皮质区域进行的不同计算。通常，仍然有剧烈的 关于皮质活动与电机系统的最终关系的关系的分歧：复杂， 在大量肌肉中，复杂的，暂时丰富的活动模式。一个基本的难题 在运动皮层（和其他地方）中的中性反应类似于某些肌肉的反应 方式但没有其他方式。我们将尝试通过两种方法来解决这种明显的悖论。首先，我们将使用 啮齿动物中的新兴方法允许从运动皮层神经元的亚群中录制，并通过 他们投射到的脊柱中间神经元的种群。这将使我们能够询问电动机的逻辑 当具有截然不同的角色的亚群被分离时，皮质反应变得更加清晰 而不是混在一起。其次，我们将使用网络理论动机的分析方法 表征人口反应的计算相关方面。这样的方法，其中许多 利用机器学习技术，保持解释本来令人困惑的方面的承诺 人口反应。这种方法可以寻求模型预测的结构，确定是否存在，以及是否存在 因此 其他皮质区域的种群）。我们还将使用网络建模来产生假设，并 探索我们方法发现的新型结构的计算相关性。初步数据指示 通过传统手段进行分析时，不同的人群看起来非常相似，但显示出截然不同的 人口级结构通过我们的新方法接近。我们相信的是 网络建模，受计算级理论启发的分析以及各种新的数据类别， 将允许确定与下游肌肉的运动皮质共享的进展，还有什么其他 计算与相关的特性运动皮层具有肌肉没有的，各种计算方式如何 相关特性在运动皮层亚群中没有 /不共享，人口响应如何 在不同的皮质区域，计算相关的维度有所不同。