Multiple time scales, coupling properties, and network interactions in respiratory rhythmicity

动物执行各种重复的行为，如呼吸和行走，而不需要有意识的控制。这种自动化是由特定的神经元集实现的，这些神经元专门产生驱动这些行为的输出。关于这些神经元如何以快速自动适应不断变化的条件的方式产生具有适当特征的活动，还有许多未回答的问题，例如从步行到跑步的转换中发生的呼吸需求的变化。 该项目使用数学和计算方法来解决呼吸中出现的几个问题。 将研究的第一组问题涉及理解在非常不同的时间尺度上演变的过程如何有助于呼吸节律。 第二组问题涉及参与呼吸特定阶段的神经元以及在不同阶段活跃的神经元群体如何协调以产生有效的呼吸节律。 这些相互作用是如何分解产生呼吸功能障碍的，特别是在雷特综合征引起的严重呼吸中断的背景下，也将进行研究。 这项工作将与实验人员合作完成，该项目的结果将导致呼吸数据的改进模型和如何对抗呼吸系统疾病的新想法。 除了增强对呼吸功能和功能障碍的理解外，该项目还将产生广泛的影响，因为涉及多个时间尺度的动态组件相互作用网络产生的节奏模式在广泛的生物和物理系统中很常见。 参与这项研究的学员将获得使用计算方法解决神经科学中数据驱动问题的经验。 制定的方法和研究结果将有助于通过当地小组会议和课程培训学生，并将通过出版物、演示和模型共享更广泛地传播。 动物与环境相互作用的各种重复行为是由耦合神经元网络的节律活动驱动的。 这个项目将通过关注哺乳动物脑干中与呼吸相关的神经元网络来解决节律的产生和控制。 在这些网络产生的节奏中，多个神经元群体在每个呼吸周期内的特定相对时间轮流激活，并且每个群体内的活动在出现时是同步的。 这项工作将分析如何同步活动与复杂的动态特征，称为爆发，在一个特定的脑干区域在呼吸的吸气阶段。 这样做将涉及对系统动态的新数学分析，这些系统的组件在几个不同的时间尺度上演变。这项工作将导致新的见解呼吸过程应该如何建模，这将是有用的研究特定的呼吸现象，如叹息，这也将推广到其他生物和物理系统与多个时间尺度的动态。 利用数学和计算方法，还将获得新的结果，即神经元网络中的同步爆发如何取决于网络中的神经元如何相互连接和相互通信。 呼吸节律必须对环境和代谢需求的变化具有鲁棒性，这些发现将产生关于什么特征提供这种鲁棒性的预测。 最后，该项目将使用分析方法，揭示特定参数变化的动态影响，以阐明特定呼吸区域内神经元的内在特性和区域之间的连接模式如何有助于整体呼吸节律和呼吸动力学的中断。 这些步骤将由新的实验数据指导，并将导致基本理解的进步以及对对抗呼吸功能障碍的有效干预措施的预测。

Animals perform a variety of repetitive behaviors, such as breathing and walking, without the need for conscious control. This automation is made possible by particular sets of neurons that are specialized to produce the outputs that drive these behaviors. There are many unanswered questions about how these neurons generate activity with the appropriate features in a way that adapts quickly and automatically to evolving conditions, such as changes in respiratory demand that occur in a switch from a walk to a run. This project uses mathematical and computational approaches to address several such questions that arise in the context of breathing. A first set of issues that will be studied relates to the understanding of how processes that evolve on very different timescales contribute to respiratory rhythms. A second set of issues relates to how neurons involved in a particular phase of respiration, as well as populations of neurons active at different phases, become coordinated to produce effective breathing rhythms. How these interactions break down to yield respiratory dysfunction, particularly in the context of the severe breathing disruptions arising in Rett syndrome, will also be studied. The work will be completed in collaboration with experimentalists, and results of the project will lead to improved models of respiratory data and novel ideas on how to counter respiratory disorders. In addition to enhancing understanding of respiratory function and dysfunction, this project will have broad implications, since rhythmic patterns produced by interacting networks of dynamic components involving multiple timescales are common across a wide range of biological and physical systems. Trainees contributing to this research will gain experience with using computational methods to address data-driven questions in neuroscience. Methods and findings developed will contribute to the training of students via local group meetings and courses and will be disseminated more broadly via publications, presentations, and model sharing. 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 address rhythm generation and control via a focus on neuronal networks in the mammalian brainstem associated with respiration. In the rhythms that these networks produce, multiple populations of neurons take turns activating at specific relative times within each breathing cycle, and the activity within each population is synchronized when it arises. This work will analyze how synchronized activity with complex dynamic features, called bursting, arises in a particular brainstem region during the inspiratory phase of breathing. Doing so will involve novel mathematical analysis of the dynamics of systems with components that evolve on several distinct timescales. This work will lead to new insights into how respiratory processes should be modeled, which will be useful for the study of particular respiratory phenomena such as sighing and which will also generalize to other biological and physical systems with multiple timescale dynamics. New results will also be attained, using mathematical and computational methods, about how synchronized bursting in neuronal networks depends on how the neurons in the network are interconnected and communicate with each other. Respiratory rhythms must be robust to changes in environmental and metabolic demands, and these findings will yield predictions about what features provide this robustness. Finally, this project will use analysis methods that reveal the dynamic effects of particular parameter variations to elucidate how the intrinsic properties of neurons within particular respiratory areas and the patterns of connections between areas contribute to overall respiratory rhythmicity and to disruptions of respiratory dynamics. These steps will be guided by novel experimental data and will result in advances in basic understanding as well as predictions about effective interventions to counter respiratory dysfunction.