Constancy and Variability in a Motor Program: CPG to Motor Performance

描述(申请人提供)：许多日常有节奏的活动，如呼吸、咀嚼和移动，部分是由称为中央模式生成器(CPGs)的神经网络编程的。对CPG的机械分析在无脊椎动物的CPG中取得了特别丰硕的成果，因为无脊椎动物的CPG具有相对较少的神经元，因此可以根据识别的神经元和特定的突触连接来准确地定义。无脊椎动物的CPG网络已经成为大脑网络如何可靠而灵活地处理感觉信息或编程运动输出的实验和理论分析的代理。我们对水蛭心跳CPG的详细分析继续对我们现在理解网络功能的组织原则做出重大贡献。不同网络和物种的理论研究和实验分析表明，神经元的固有膜特性及其突触连接的强度在动物之间表现出2-5倍的可变性，但网络仍然产生功能输出。然而，有明确的例子--包括我们自己的工作--表明，突触和内在属性的个体差异确实会产生功能后果，体现在网络性能或对扰动的敏感性上。这些研究表明，为了从根本上理解神经元网络，我们必须努力从个体那里收集完整的数据，因为来自个体的网络，虽然在功能上是刻板的，但在膜电流和突触连接水平上可能有不同的机制基础，具有明显的功能后果。鉴于这种变化，有必要采用集合建模的方法来生成和测试关于生活网络的假设。在该方法中，使用了由进化算法或强力参数变化生成的多个模型实例(具有不同的参数)。这种方法的先决条件是进行实验研究，以确定各种网络中生命系统中的参数变化。我们的实验将结合计算和神经生理学方法来阐明网络功能的基本机制以及网络参数中个体差异的影响。此外，我们还将利用个体差异作为探索网络功能的创新工具。水蛭心跳CPG类似于在运动神经元中产生协调节律活动的脊髓CPG，其相对简单和卓越的可及性使细胞分析达到目前在脊髓中无法实现的水平。我们将分析CPG输入和运动神经元固有特性的个体间差异对运动输出的影响，以及运动如何反映这种差异。这些研究将提供对个体网络功能的基本机械见解，这将有助于脊髓和大脑网络的研究，并可能导致治疗脊髓和脑损伤以及影响运动能力的疾病。

DESCRIPTION (provided by applicant): Many everyday rhythmic activities such as breathing, chewing, and locomoting, are programed in part by neuronal networks called central pattern generators (CPGs). Mechanistic analysis of CPGs has been especially fruitful in the CPGs of invertebrates, which have relatively few neurons and can therefore be precisely defined in terms of identified neurons and specific synaptic connections. CPG networks of invertebrates have become proxies for experimental and theoretical analyses of how brain networks reliably yet flexibly process sensory information or program motor output. Our detailed analyses of the leech heartbeat CPG continue to contribute substantially to the organizing principles by which we now understand network function. Theoretical studies and experimental analysis in different networks and species have shown that the intrinsic membrane properties of the neurons and the strengths of their synaptic connections show 2-5 fold animal-to-animal variability, yet networks still produce functional output. Nevertheless, there are clear examples - including from our own work - showing that individual variation in synaptic and intrinsic properties do have functional consequences that manifest in network performance or susceptibility to perturbation. These studies imply that to understand fundamentally a neuronal network, we must strive to gather complete data from individuals because networks from individuals, while functionally stereotyped, may have different mechanistic underpinnings at the level of membrane currents and synaptic connections with discernible functional consequences. In light of this variation, an ensemble modeling approach is necessary to generate and test hypotheses about living networks. In this approach, multiple model instances (with different parameters) generated by evolutionary algorithms or brute force parameter variation are used. A prerequisite for this approach is experimental studies that determine parameter variation in the living system across a variety of networks. Our experiments will integrate computational and neurophysiological approaches to elucidate basic mechanisms of network function and the impact of individual variation in network parameters. Moreover, we will use individual variation as an innovative tool to probe network function. The leech heartbeat CPG is analogous to spinal CPGs that produce coordinated rhythmic activity in motor neurons, and its relative simplicity and superb accessibility allow a level of cellular analysis not currently possible in spinal cord. We will analyze the impact of inter-individual variation in CPG input and motor neuron intrinsic properties on motor output and how movements reflect this variation. These studies will provide basic mechanistic insights into network function in individuals, which will be useful in the study of spinal and brain networks and can lead to therapies for spinal cord and brain injury and disorders affecting motor performance.