Central Pattern Generation: Circuit Analysis and Modeling

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Rhythmic oscillating activity is a property of many biological systems, and we are still far from understanding the theoretical basis for mechanisms driving such rhythms. In the nervous systems of many animals, networks that produce oscillations often have to be stable, yet capable of modulation or switching between different modes. These networks often have individual properties that have made it difficult to sort out what key elements are responsible for stability in oscillators. This project develops new analytical methods to predict oscillatory patterns exhibited by neural circuits composed of physiological neurons (living nerve cells, not just modeled ones), using only data that are readily obtainable from the isolated component neurons. A novel approach here uses computational analysis of phase-resetting curves to make theoretical predictions of network behavior, and to test whether these theoretical methods apply to physiological data from small neuronal circuits in crustaceans. Results will have a substantial impact on understanding oscillating systems in the brain (such as those for locomotion, respiration, and sleep-wake cycles), and could extend beyond neuroscience into areas such as ecology and even non-living physical oscillating systems. This project also develops a fruitful collaboration among four women scientists at a range of levels in the computational field, where they are under-represented, with consequent opportunities for mentoring, and an impact on women's careers.