Generation and control of rhythmic activity in respiratory and motor networks

动物与环境相互作用的各种基本重复行为是由耦合神经元网络的节律性活动驱动的。该项目将重点研究与呼吸和肢体运动这两类重复运动相关的神经网络节律的产生和控制。在神经元节律中，多个神经元群体在每个周期中依次激活，并且每个群体中的活动在出现时是同步的。这项工作将探索在呼吸吸气阶段发生的pre-Bötzinger复合体（preBötc）中呼吸神经元同步破裂的机制。这样做将涉及对三个时间尺度动力学和单个神经元中多个爆发生成机制相互作用的新颖数学分析。关于神经元网络的同步爆发如何依赖于网络耦合的特征和所涉及的神经元的内在性质，将获得新的结果。额外的建模和分析将考虑preBötc如何与其他呼吸区域的神经元相互作用，并参与闭环反馈控制系统，以实现灵活响应变化需求的鲁棒呼吸节律。在肢体运动领域，控制多肢节段和肢体的肌肉群的协调是有效行为的关键。该项目将分析一个特定关节的正确定时节律活动是如何从自上而下的肌肉激活神经命令与来自其他关节运动的反馈信号的相互作用中产生的。我们还将研究不同的刺激如何重新配置特定的节奏产生电路，以产生单个肢体的不同运动，这是行为灵活性所需要的。所进行的分析将为如何处理多时间尺度动力系统中的强迫提供新的结果，并将提出神经网络中协调节奏产生的一般机制。呼吸和运动是许多有节奏的神经机械过程之一，这些过程可以在没有直接意识控制的情况下维持。这种自动化是由大脑和脊髓中的特定神经元实现的，这些神经元专门产生驱动这些行为的信号。关于这些神经元如何产生具有适当特征的活动，并以一种灵活的方式适应变化的条件，比如行走时遇到的地形变化，还有许多悬而未决的问题。该项目将利用受实验数据约束的数学模型的发展，以及对这些模型的计算机模拟和数学分析来解决几个这样的问题。在呼吸的背景下，这个项目的结果将有助于解释不同组的呼吸神经元如何以正确的强度和时间产生输出，以驱动正常的呼吸。他们还将对与血液中气体水平和肺和胸壁拉伸量相关的信号如何反馈以调整神经元输出并在不断变化的呼吸需求下保持成功的呼吸，以及该系统如何在某些呼吸障碍中失败提供新的理解。这些结果将与实验员合作获得，实验员将提供直接访问数据和模型预测测试的机会。该项目还将研究与重复肢体运动相关的两类问题。首先，行走等行为需要激活多个肌肉群，以适当的顺序控制多个肢体节段和关节。我们将使用建模和数学来探索如何实现这种协调。其次，动物通过以各种不同的模式激活肢体控制肌肉，利用一小部分肢体来实现各种各样的行为。我们将使用数学分析来验证假设，即单个神经元节律生成网络可以生成多个这样的模式，这些模式由触发不同行为的输入信号选择。我们的分析将为我们的实验合作者提供预测，以更好地理解肢体运动是如何产生的，这可以为机器人技术提供有用的信息，并为恢复因疾病或受伤而受损的肢体运动提供帮助。

A variety of repetitive behaviors fundamental to animals' interactions with the environment are driven by the rhythmic activity of networks of coupled neurons. This project will focus on the generation and control of rhythms in neuronal networks associated with two classes of repetitive movements, namely respiration and limb motion. In the neuronal rhythms, multiple populations of neurons activate sequentially within each cycle, and the activity within each population is synchronized when it arises. This work will explore the mechanisms underlying the synchronized bursting of respiratory neurons in the pre-Bötzinger complex (preBötc), which occurs during the inspiratory phase of breathing. Doing so will involve novel mathematical analysis of three time scale dynamics and of the interaction of multiple burst-generation mechanisms in single neurons. New results will be attained about how synchronized bursting in neuronal networks depends on features of network coupling and on intrinsic properties of the neurons involved. Additional modeling and analysis will consider how the preBötc interacts with neurons in other respiratory areas and participates in a closed loop feedback control system to achieve robust respiratory rhythms that respond flexibly to changing demands. In the area of limb motion, coordination of muscle groups controlling multiple limb segments and limbs is critical for effective behaviors. This project will analyze how correctly timed rhythmic activity of a particular joint emerges from the interaction of top-down neural commands for muscle activation with feedback signals from movements of other joints. We will also study how different stimuli can reconfigure a particular rhythm generation circuit to yield diverse movements of a single limb, as needed for behavioral flexibility. The analysis performed will provide new results on how to handle forcing in multiple time scale dynamical systems and will suggest general mechanisms that underlie coordinated rhythm generation in neuronal networks. Respiration and locomotion are among the many rhythmic neuro-mechanical processes that can be maintained without direct conscious control. This automation is made possible by particular sets of neurons in the brain and spinal cord, which are specialized to produce the signals that drive these behaviors. There are many unanswered questions about how these neurons generate activity with the appropriate features in a way that adapts fluidly to altered conditions, such as changes in terrain encountered while walking. This project will use the development of mathematical models constrained by experimental data as well as computer simulations and mathematical analysis of these models to address several such questions. In the context of respiration, the results of this project will help explain how output from different groups of respiratory neurons is produced with the correct intensity and timing to drive normal breathing. They will also provide new understanding of how signals related to the levels of gases in the blood and the amount of stretch in the lungs and chest wall feed back to tune the neuronal outputs and maintain successful breathing under changing respiratory demands, as well as how this system may fail in certain breathing disorders. These results will be attained in collaboration with experimentalists, who will provide direct access to data and testing of model predictions. The project will also study two classes of problems related to repetitive limb movements. First, behaviors such as walking require activation of multiple muscle groups, controlling multiple limb segments and joints, in an appropriate sequence. We will use modeling and mathematics to explore how this coordination is achieved. Second, animals achieve a diverse range of behaviors using a small set of limbs by activating their limb control muscles in a variety of different patterns. We will use mathematical analysis to test the hypothesis that a single neuronal rhythm generation network can generate multiple such patterns, selected by input signals that trigger different behaviors. Our analysis will supply predictions that can be tested by our experimental collaborators to gain a better understanding of how limb motions are generated, which can provide useful information for robotics and for efforts to restore limb movements compromised by disease or injury.