Linear Conductance-Based Mechanisms Underlying Oscillations in Neuronal Networks

这个项目的主要目标是提供对产生稳定的慢爆振荡至关重要的神经元属性的新理解，并探索当表达它们的神经元嵌入网络时它们被激活的后果。非线性再生内向电流被认为是产生慢振荡和爆发的重要因素。该项目使用了一种新的方法，其中用负电导的线性电流来代替非线性的内向电流。这一方法揭示了再生内向电流所扮演的多重角色，并简化了对其他离子流对振荡活动的贡献的研究。它还允许对产生神经元振荡的机制进行更简单的数学分析。这种方法被用来研究特定离子电流在单个神经元振荡的产生和形成中的作用，检查神经元之间的突触相互作用如何与固有特性合作或竞争以在由不同种类神经元组成的网络中产生振荡，并确定当神经元是网络的一部分时，在孤立细胞中引起振荡的机制是否保持不变。这些目标是利用简化数学模型中的动力学系统和分叉理论技术以及详细的生物物理模型的模拟和对螃蟹幽门网络爆裂神经元的电生理实验来实现的。这三种方法是并行发展的，允许在理论和实验方法之间不断交换结果。从运动到认知任务的大量行为依赖于大脑中神经元网络产生的振荡活动。尽管大脑振荡具有优势和不可缺少的作用，但很少有理论工具可以用来理解这种振荡是如何产生或控制的。使用了一种新的方法，将生物实验和数学分析相结合，将网络组件中存在的复杂相互作用分解为简单的构建块。这允许提取在产生振荡中重要的核心元素，并将使用数学模型阐明其他现有组件在雕刻行为中的作用。通过将实时计算机模拟的神经元与螃蟹中枢神经系统中的小型振荡网络连接起来的实验，对这些模型进行了测试。该项目为开发基于神经的控制系统提供了一个框架，该系统在机器人学和仿生计算中具有潜在的应用。

The primary goal of this project is to provide a new understanding of the neuronal properties that are critical for producing stable slow bursting oscillations and to explore the consequences of their activation when neurons expressing them are embedded in a network. Nonlinear regenerative inward currents are thought to be important in producing slow oscillations and bursting. This project uses a novel approach where the nonlinear inward current is replaced with a linear current of negative conductance. This approach exposes the multiple roles played by regenerative inward currents and simplifies the investigation of the contribution of other ionic currents to oscillatory activity. It also allows for a simpler mathematical analysis of the mechanisms that generate neuronal oscillations. This methodology is used to investigate the role of specific ionic currents in the generation and shaping of oscillations in individual neurons, to examine how synaptic interactions between neurons cooperate or compete with intrinsic properties to produce oscillations in networks of heterogeneous neurons, and to determine if the mechanisms that give rise to oscillations in isolated cells remain unchanged when the neuron is part of a network. These aims are pursued using techniques of dynamical systems and bifurcation theory in reduced mathematical models as well as simulations of detailed biophysical models, and electrophysiological experiments on bursting neurons of the crab pyloric network. These three methods are developed in parallel allowing for continual exchange of findings between the theoretical and experimental approaches.Numerous behaviors ranging from locomotion to cognitive tasks rely on oscillatory activity generated by networks of neurons in the brain. Despite the predominance and indispensability of brain oscillations, few theoretical tools are available for understanding how such oscillations are generated or controlled. A novel approach is used that combines biological experiments and mathematical analysis to break apart the complex interactions present in network components into simple building blocks. This allows core elements that are important in the generation of oscillations to be extracted and will clarify the role of other existing components in sculpting behavior using mathematical models. The models are tested through experiments that connect real time computer-simulated neurons to small oscillatory networks in the crab central nervous systems. This project provides a framework for developing neural-based control systems with potential applications in robotics and bio-inspired computing.