Modeling the Brainstem Neural Mechanisms for the Respiratory Pattern Generation

大脑整合和协调多层次组织的神经处理，以产生行为。本项目将呼吸的神经控制作为一个系统，对细胞、网络和系统神经机制的跨层整合进行计算建模。总体目标是在一个统一的框架内建立一个统一的多层次呼吸神经控制模型，以结合现有的数据和当前关于呼吸控制的假设。呼吸振荡是主要由内源性细胞‘起搏器’活动产生的，还是由网络电路中的兴奋和抑制特性引起的，目前仍存在争议。该项目模拟单个呼吸神经元的膜活动以研究爆发活动；模拟中央模式发生器(CPG)网络的整个电路，以研究连接如何以真实的放电模式和外部扰动的变化导致稳定的呼吸节奏；并详细阐述CPG模型以在模拟条件下产生起搏器驱动的节奏。在计算模型中研究了用于产生节律的起搏器驱动状态和基于网络的状态之间的转换的机制和条件，并直接与来自其他实验室的实验生物学数据进行比较。研究结果将澄清呼吸控制这一重要话题的基本方面，可能解决当前的争议，并将产生超出基础神经科学的影响，最终影响到包括生理学控制系统分析在内的生物医学工作。这种复杂的多尺度方法是新颖的，将提供有价值的博士后和学生培训。

The brain integrates and coordinates neural processing across multiple levels of organization to produce behavior. This project takes the neural control of breathing as a system for computational modeling of cross-level integration of cellular, network and systems neural mechanisms. The overall objective is to build a united multi-level model of neural control of respiration, within a uniform framework to incorporate existing data and current hypotheses on respiratory control. It remains controversial whether respiratory oscillation is produced primarily by endogenous cellular 'pacemaker' activity, or instead by properties of excitation and inhibition within a network circuit. This project models membrane activity for single respiratory neurons to investigate bursting activity; models the whole circuitry of the central pattern generator (CPG) network to investigate how connectivity can result in a steady respiratory rhythm with realistic firing patterns and changes from external perturbations; and elaborates the CPG model to generate a pacemaker-driven rhythm under simulated conditions. Mechanisms and conditions for the transition between pacemaker-driven and network-based states for generating rhythms are investigated in computational models and directly compared to experimental biological data from other laboratories. Results will clarify fundamental aspects of the important topic of respiratory control, may settle a current controversy, and will have an impact beyond basic neuroscience, eventually to biomedical work including control-systems analysis of physiology. The complex multi-scale approach is novel, and will provide valuable postdoctoral and student training.