PHYSIOLOGICAL AND COMPUTATIONAL APPROACHES TO UNDERSTANDING NEURONAL SYNCHRONIZAT

理解神经元同步的生理学和计算方法

• 批准号: 8092724
• 负责人: Nathan Neal Urban
• 金额: $ 30.4万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8092724
• 关键词: Address; Area; Biological Models; Biological Neural Networks; Brain; Cells; Computer Simulation; Data; Disease; Environment; Frequencies; Funding; Generations; Goals; Health; Heterogeneity; Mus; Neurons; Noise; Pattern; Phase; Physiological; Physiology; Play; Population; Potassium; Property; Recurrence; Role; Sensory; Shapes; Source; Stimulus; Synapses; System; Systems Analysis; Testing; Work; biological systems; improved; interest; novel; olfactory bulb; olfactory stimulus; relating to nervous system; research study; sensory stimulus; simulation

DESCRIPTION (provided by applicant): We are interested in how high frequency (30+ Hz) brains oscillations are generated using the mouse olfactory bulb as our model system. These oscillations are a prominent feature of neural activity in many brain areas including the olfactory system. The mechanisms that underlie these oscillations, as well as the functional role they play are poorly understood, but they have recently been implicated in many aspects of brain function and disease. In the previous funding period, we have described a novel mechanism, called stochastic synchrony, by which correlated fast fluctuating inputs can generate synchronous gamma-frequency (30-80Hz) oscillations in populations of neurons. While much of our previous work used olfactory bulb neurons as a model system for analysis of this novel mechanism of synchronization, this prior work ignored many details of olfactory bulb neurons and circuits in order to describe the general features of this phenomenon. In this application we propose to extend our previous work by considering how key features of olfactory bulb circuitry and physiology modulate stochastic synchrony. We are particularly interested in whether the observed heterogeneity of neural properties is a useful feature of neural networks or is a "bug" that results from the intrinsic imprecision of biological systems. Homogeneity should enhance synchrony but recent data suggests that heterogeneity across neurons of the same type may provide certain functional advantages. We are also interested in how the synaptic connectivity of the olfactory bulb may facilitate or disrupt synchrony. Exploring these mechanisms will improve our understanding of the function and disorders of synchrony, especially in the context of the vertebrate olfactory system PUBLIC HEALTH RELEVANCE: Activity of neurons in the olfactory system represents information about sensory stimuli in the environment. One of our long term goals is to understand how to interpret features of neuronal activity as representing features of olfactory stimuli. To accomplish this goal we believe that it is critical to develop our understanding of the dynamics of neuronal activity through the use of experiments and computational models. In this proposal we describe an integrated experimental and computational approach to analysis of the mechanisms of patterns of periodic synchronized activity in the olfactory system. In particular we want to understand how the detailed and diverse biophysical and synaptic properties of olfactory bulb neurons contribute to the generation of patterned activity.

描述（由申请人提供）：我们感兴趣的是如何使用小鼠嗅球作为我们的模型系统产生高频（30+ Hz）脑振荡。 这些振荡是包括嗅觉系统在内的许多大脑区域的神经活动的显著特征。 这些振荡背后的机制以及它们所起的功能作用还知之甚少，但最近它们与大脑功能和疾病的许多方面有关。 在上一个资助期，我们描述了一种称为随机同步的新机制，通过这种机制，相关的快速波动输入可以在神经元群体中产生同步的伽马频率（30- 80 Hz）振荡。 虽然我们以前的大部分工作使用嗅球神经元作为模型系统来分析这种新的同步机制，但为了描述这种现象的一般特征，以前的工作忽略了嗅球神经元和电路的许多细节。 在这个应用程序中，我们建议扩展我们以前的工作，考虑如何嗅球电路和生理的关键功能调制随机同步。 我们特别感兴趣的是，观察到的神经特性的异质性是否是神经网络的一个有用的功能，或者是一个“错误”，从生物系统的内在不精确性的结果。 同质性应该增强同步性，但最近的数据表明，同一类型的神经元之间的异质性可能提供某些功能优势。 我们也对嗅球的突触连接如何促进或破坏同步感兴趣。 探索这些机制将提高我们对同步性功能和障碍的理解，特别是在脊椎动物嗅觉系统的背景下 公共卫生相关性：嗅觉系统中神经元的活动代表了有关环境中感官刺激的信息。 我们的长期目标之一是了解如何将神经元活动的特征解释为代表嗅觉刺激的特征。 为了实现这一目标，我们认为，通过使用实验和计算模型来发展我们对神经元活动动力学的理解是至关重要的。 在这个建议中，我们描述了一个综合的实验和计算方法来分析模式的周期性同步活动的嗅觉系统的机制。 特别是，我们想了解嗅球神经元的详细和多样的生物物理和突触特性如何有助于产生模式化的活动。