Generation and control of rhythmic activity in respiratory and motor networks

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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.