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Data Reduction Techniques for Systematic Information Quantification in Large Scale, Multiple Spike Trains

大规模、多尖峰序列系统信息量化的数据缩减技术

基本信息

批准号：
EP/E057101/1
负责人：
Hujun Yin
金额：
$ 6.56万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2007
资助国家：
英国
起止时间：
2007 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FE057101%2F1
关键词：
Data Reduction Techniques Systematic Information

项目摘要

It is widely believed that the function of the brain crucially depends on the interaction between large numbers of different neuronal populations located in different brain areas. To test empirically how these neuronal populations work together to generate functions such as sensation and perception, neuroscientists record simultaneously the activity of different neuronal populations in the brain. Recording of this activity is achieved by a number of different methods. Multi-electrodes have become a standard tool for studying the simultaneous activity of multiple neurons in a specific brain region or across different regions. The stimulus information encoded in the spike trains is a primary focus of research in neuroscience and is often examined in terms of various responses or features such as spike counts, mean response time, first spike latency, as well as interspike intervals and firing rate. The massive response data arrays recorded in growing number of experiments pose many challenges for data analysis and for interpreting and modelling of neuronal functions. In this feasibility study we propose to bring together the information theoretic expertises in the systems engineering, data-reduction techniques and the multivariate statistics in order to provide a method to analyse most effectively the information content of large neural populations. In particular, we consider spike trains as an information encoding and decoding process and propose to use wavelets, ICA and topographical mappings to extract, classify, quantify and organise information features contained in spike trains under the information theoretic framework; and we propose to use self-organised data reduction techniques for improved estimation of mutual information, and also to explore a number of different approaches to provide information-theoretic methods suited to the combined analysis of brain signals of different nature.

人们普遍认为，大脑的功能关键取决于位于不同脑区的大量不同神经元群体之间的相互作用。为了经验性地测试这些神经元群体如何协同工作来产生感觉和知觉等功能，神经科学家同时记录了不同神经元群体在大脑中的活动。这项活动的记录有多种不同的方法。多电极已经成为研究特定脑区或跨不同区域的多个神经元同时活动的标准工具。棘波序列中编码的刺激信息是神经科学研究的主要焦点，通常根据各种反应或特征进行检查，如棘波计数、平均反应时间、第一个棘波潜伏期以及棘波间期和放电率。在越来越多的实验中记录的大量响应数据阵列给数据分析以及神经元功能的解释和建模带来了许多挑战。在这项可行性研究中，我们建议将系统工程、数据约简技术和多元统计方面的信息理论专家结合起来，以便提供一种最有效地分析大型神经群体的信息含量的方法。特别是，我们将棘波训练视为一个信息编码和解码过程，并建议在信息论框架下使用小波、独立分量分析和地形映射来提取、分类、量化和组织棘波训练中包含的信息特征；我们建议使用自组织数据约简技术来改进互信息估计，并探索一些不同的方法来提供适合于不同性质的脑信号的组合分析的信息论方法。