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

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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.