Walking, swimming, flying, burrowing and chewing are rhythmic behaviors that allow animals to survive and reproduce. These behaviors remain effective even in the presence of unexpected perturbations or noise. The investigators hypothesize that the robustness of a pattern generator is primarily mediated by the interplay of neural dynamics and sensory input. This hypothesis will be tested by (1) studying in vivo responses of a feeding pattern generator to mechanical perturbations in the marine mollusk Aplysia californica, whose identified neurons and well-studied biomechanics make it especially experimentally tractable, (2) using theoretical, computational and mathematical tools to develop insight into dynamical architectures of robustness, such as a globally stable limit cycles, or stable heteroclinic channels and (3) directly testing the central hypothesis using a semi-intact preparation that can generate behavior, and can respond to mechanical perturbations, to determine the role of identified sensory neurons in generating appropriate responses to these perturbations by selectively activating or inhibiting the neurons.
Developing an understanding of robust dynamical architectures would have many applications. In particular, the research will open up the possibility of creating control architectures for robots that can flexibly cope with unpredictable environmental changes, and successfully pursue long-term goals despite environmental perturbations. It will play an important role in developing robust prosthetic devices that cope flexibly with everyday tasks, simplifying the process of rehabilitation. Additionally, this project enhances the efforts of the lead investigator, a neurobiologist, and the co-investigator, a mathematician, to co-mentor students in the interdisciplinary area of mathematical and computational neuroscience, and also impacts the content of the interdisciplinary courses that they teach.
Many behaviors that we take for granted - like walking, breathing, or eating - are rhythmic, but can change flexibly on a moment to moment basis if conditions in the environment change. For example, if one is feeding on food that is unexpectedly soft or tough, the nervous system can adjust rapidly, so that we can continue to eat without conscious effort. How does the nervous system achieve this remarkable flexibility? To address this question, we developed a mathematical model of an experimentally tractable feeding system, and used it to develop new insights into how a rhythmic pattern generator can continue to perform reliably while responding flexibly to unexpected changes in sensory inputs. We used the model to predict how a feeding apparatus could respond to an unexpected change in mechanical load - equivalent to encountering food that was tougher than expected - and then showed that its predictions matched the behavioral response of the biological feeding system. The research has had several important broader impacts. First, it contributed to the training of undergraduate students, graduate students, and postdoctoral fellows who are committed to careers in science and mathematics. Second, it has led to new approaches to understanding pattern generators, which is likely to have a broad impact on the study of this fundamental biological question. Third, the novel mathematical model that can incorporate sensory input has been used to control soft-bodied robots, which may find broad applications in industry and medicine.
