Differential and Integral Equations in Neurobiology

神经生物学中的微分方程和积分方程

• 批准号: 0513500
• 负责人: Bard Ermentrout
• 金额: --
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0513500&HistoricalAwards=false
• 关键词: Differential; Integral; Equations; Neurobiology

Methods from nonlinear dynamics are used to study the coupling between oscillators and non-oscillators in model neuronal networks. Specifically, averaging is used to analyze the termination of waves in spatially connected networks by reducing the complicated conductance-based models to scalar spatial models. Phase-plane methods are applied to the reduced systems. Averaging is also used at the single spike level to understand the transition between synchrony and asynchrony in coupled networks that have different types of connections. Population density methods and linearized stability analysis will distinguish between the onset of synchrony, clusters, and propagating waves in spatially distributed networks of neural oscillators. Indirect coupling between excitable and oscillatory cells will be analyzed using a combination of phase-resetting curves and the dispersive properties of excitable cables.When neurons are connected together, they can often produce persistent activity. Such persistent activity has been implicated in short-term memory -- how an animal or human "holds a thought." What kinds of interactions disrupt this and which are necessary to maintain the activity are some of the questions that are asked in this proposal. When activity is too persistent then certain pathologies arise such as epilepsy. Thus, one goal of this proposal is to understand how to strike a balance between the ability to produce stable persistent activity while preventing its pathological propagation into quiet regions. When neurons fire they communicate with other neurons and depending on the interactions, the result can be that the neurons want to fire together or they want to fire asynchronously. The latter is useful for persistent activity. Synchrony on the other hand is crucial for several normal physiological processes. For example, it is known that certain cells in the base of the brain organize the output of hormones. The electrical activity of these cells is synchronized yet the mechanisms for this synchrony remain unknown since there are no direct connections between the individual oscillating neurons. We will study mechanism through which indirect coupling can produce synchronous behavior. Tools and methods developed in this proposal will have applications well beyond neuroscience, as the questions of synchrony and propagation of "information" are ubiquitous in biology from the single cell level on up to the ecological interactions between populations.

利用非线性动力学的方法研究了模型神经元网络中振子与非振子之间的耦合。具体而言，平均是用来分析波的终止在空间连接的网络，减少复杂的电导为基础的模型，标量空间模型。相平面法应用于简化系统。平均也用于单个尖峰水平，以了解具有不同类型连接的耦合网络中同步和同步之间的转换。种群密度方法和线性稳定性分析将区分同步的开始、集群和空间分布的神经振荡器网络中的传播波。可兴奋细胞和振荡细胞之间的间接耦合将使用相位重置曲线和可兴奋电缆的色散特性进行分析。当神经元连接在一起时，它们通常会产生持续的活动。 这种持续的活动与短期记忆有关--动物或人类如何"持有一个想法"。"什么样的相互作用会破坏这一点，哪些是维持活动所必需的，这是本提案中提出的一些问题。当活动过于持久时，就会出现某些病理，如癫痫。因此，该提议的一个目标是了解如何在产生稳定持续活动的能力与防止其病理传播到安静区域之间取得平衡。当神经元激发时，它们与其他神经元进行通信，根据相互作用，结果可能是神经元想要一起激发或想要异步激发。后者对持续活动很有用。另一方面，同步性对几个正常的生理过程至关重要。例如，已知大脑底部的某些细胞组织激素的输出。这些细胞的电活动是同步的，但这种同步的机制仍然未知，因为各个振荡神经元之间没有直接联系。 我们将研究间接耦合产生同步行为的机制。在这个提议中开发的工具和方法的应用将远远超出神经科学，因为“信息”的同步和传播问题在生物学中无处不在，从单细胞水平到种群之间的生态相互作用。