Central Pattern Generation: Circuit Analysis and Modeling

有节奏的振荡活动是许多生物系统的特性，我们还远远没有理解驱动这种节律的机制的理论基础。在许多动物的神经系统中，产生振荡的网络通常必须是稳定的，并且能够在不同的模式之间进行调制或切换。这些网络通常具有单独的特性，这使得很难找出哪些关键因素对振荡器的稳定性负责。该项目开发了新的分析方法来预测由生理神经元（活的神经细胞，而不仅仅是建模的神经细胞）组成的神经回路所表现出的振荡模式，仅使用从分离的组成神经元中容易获得的数据。本文采用一种新颖的方法，对相位重置曲线进行计算分析，对网络行为进行理论预测，并测试这些理论方法是否适用于甲壳类动物小神经元回路的生理数据。研究结果将对理解大脑中的振荡系统（如运动、呼吸和睡眠-觉醒周期的振荡系统）产生重大影响，并可能扩展到神经科学以外的领域，如生态学，甚至是非生命物理振荡系统。该项目还在计算领域的各个层次上发展了四位女科学家之间富有成效的合作，这些领域的代表人数不足，因此有机会获得指导，并对妇女的职业生涯产生影响。

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.