Linear Conductance-Based Mechanisms Underlying Oscillations in Neuronal Networks

神经元网络中基于线性电导的振荡机制

• 批准号: 1122291
• 负责人: Amitabha Bose
• 金额: $ 47.1万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1122291&HistoricalAwards=false
• 关键词: Linear; Conductance; Based; Mechanisms; Underlying

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.

这个项目的主要目标是提供一个新的理解的神经元的特性是至关重要的产生稳定的缓慢爆发振荡，并探讨其激活的后果时，表达它们的神经元嵌入在网络中。 非线性再生内向电流被认为在产生缓慢振荡和爆发中是重要的。 该项目使用了一种新的方法，其中非线性内向电流被替换为负电导的线性电流。 这种方法暴露了再生内向电流所起的多重作用，并简化了其他离子电流对振荡活动的贡献的调查。 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。 该方法用于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，研究神经元之间的突触相互作用如何与内在特性合作或竞争以在异质神经元网络中产生振荡，并确定当神经元是网络的一部分时，引起孤立细胞振荡的机制是否保持不变。 这些目标是追求使用技术的动力系统和分叉理论减少数学模型，以及模拟详细的生物物理模型，和电生理实验的螃蟹幽门网络爆裂神经元。 这三种方法是并行开发的，允许理论和实验方法之间不断交换发现。从运动到认知任务的许多行为都依赖于大脑神经元网络产生的振荡活动。 尽管脑振荡的优势和不可或缺，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模块。 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。 该模型通过实验进行测试，连接真实的时间计算机模拟的神经元，在螃蟹中枢神经系统的小振荡网络。 该项目提供了一个框架，开发基于神经的控制系统，在机器人和生物启发计算的潜在应用。