Analysis of Neuronal Spike Trains using Prototype Point Processes

使用原型点过程分析神经元尖峰序列

• 批准号: 0907708
• 负责人: Frederic Schoenberg
• 金额: $ 10万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0907708&HistoricalAwards=false
• 关键词: Analysis; Neuronal; Spike; Trains; using

The investigator develops tools for the summary and description of datasets involving catalogs of multiple realizations of point processes, using non-parametric techniques involving point process prototypes. Particular attention is paid to the case where each realization is a list of points in space and time, and a major characteristic of this proposed research involves the extension of prototypes and analyses based on metrics such as spike-time distance from one-dimensional point processes to the case of repeated realizations of point processes in higher dimensions. For such datasets, prototypes represent a useful summary of the behavior of the typical realization of the point process, and as such can be used to compare realizations from distinct classes of point processes. The methods investigated are entirely non-parametric, relying on minimal assumptons about the point processes being studied. The investigators apply their techniques to the neuroscience problem of summarizing and describing patterns in neuronal cell firings in conditioned and unconditioned subjects responding to various stimuli and to the description of typical spatial-temporal incidence of wildfires within seasons or years, as well as the characterization of other point process datasets such as earthquake catalogs. The methods developed in this proposal are useful for detecting and summarizing patterns in data from a wide variety of applications where one records repeated observations of phenomena that seem to occur at random times and locations. Examples include the firings of neurons within the brain, global earthquakes and their aftershocks, and the locations and times of wildfire ignitions in Southern California. Prototype methods are incredibly useful for the description of such catalogs and, in the context of earthquakes and wildfires, can aid urban planners and emergency response personnel, structural engineers and those interested in insurance in describing what to expect in a typical realization. In the neuroscience setting, this project contributes to the understanding of the brain and its properties, as the methods explored here help to characterize the typical neuronal firing patterns among subjects receiving different stimuli or different forms of conditioning.

研究人员使用涉及点过程原型的非参数技术，开发用于总结和描述数据集的工具，该数据集涉及点过程的多个实现的目录。特别注意的是每个实现都是空间和时间中的点的列表的情况，本研究的一个主要特征涉及原型的扩展和基于度量的分析，例如从一维点过程的尖峰时间距离到更高维点过程的重复实现的情况。对于这样的数据集，原型代表了点过程的典型实现的行为的有用总结，因此可以用来比较来自不同类别的点过程的实现。所研究的方法完全是非参数的，依赖于所研究的点过程的最小假设。研究人员将他们的技术应用于神经科学问题，总结和描述条件受试者和非条件受试者对各种刺激的神经元放电模式，描述季节或年份内野火的典型时空发生率，以及其他点过程数据集的特征，如地震目录。这项建议中开发的方法对于检测和总结来自各种应用的数据中的模式很有用，在这些应用中，人们记录了对似乎在随机时间和地点发生的现象的重复观察。例如，大脑内神经元的燃烧，全球地震及其余震，以及南加州野火点燃的地点和时间。原型方法对于描述这类目录非常有用，在地震和野火的背景下，可以帮助城市规划者和应急人员、结构工程师和那些对保险感兴趣的人描述典型实现中的预期。在神经科学的背景下，这个项目有助于理解大脑及其属性，因为这里探索的方法有助于描述接受不同刺激或不同形式的条件反射的受试者之间的典型神经元放电模式。