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

摘要 自20世纪60年代末S以来，一大批文献试图刻画神经反应的特性 大脑的运动区，并将这些反应与外部测量的变量(如肌肉)联系起来 活动或达到方向。在某些方面，这一领域非常成功：早期研究显示 在相互连接的区域的广泛网络中，与运动相关的神经放电率的调制。 这种活动是广泛调节的，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 一小部分反应神经元的性质。然而，这种人口水平计算的本质是 仍然存在争议。即使是初级运动皮质也是如此，这使得它更难被定性 并对比不同皮质区域的不同计算结果。总体而言，中国仍保持着旺盛的 关于皮质活动与运动系统终极的关系的分歧：复杂， 在一大群肌肉中复杂的、暂时丰富的活动模式。一个根本的难题 运动皮质(和其他部位)的神经反应类似于某些肌肉的反应 只有一种方式，其他人没有。我们将试图通过两种方法来解决这一明显的悖论。首先，我们将使用 啮齿动物中新出现的方法，允许记录运动皮质神经元的亚群，通过 它们投射到的脊髓中间神经元的数量。这将允许我们问一问，马达的逻辑 当可能具有非常不同作用的亚群被分离时，皮质反应变得更加清晰 而不是混为一谈。其次，我们将使用网络理论激励的分析方法来 描述人口反应在计算上相关的方面。这种方法，其中许多方法 利用机器学习技术，有希望解释 人口反应。这样的方法可以寻找模型预测的结构，确定它是否存在，以及是否 因此，无论它在不同的人群中是否存在差异(即，运动皮质内的亚群和 其他皮质区域的人口)。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结构的计算相关性。初步数据显示 不同的人群在通过传统方法分析时可能看起来非常相似，但表现出非常不同的情况 通过我们的新方法来处理人口层面的结构问题。我们相信这是一种 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， 将允许在确定运动皮质与下游肌肉共享什么方面取得进展， 在计算方面，运动皮质具有肌肉所没有的相关特性，在计算上如何不同 相关特性在运动皮质亚群中共享/不共享，以及种群如何反应 在不同的皮质区域，在计算相关的维度上是不同的。

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.