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

• 批准号: EP/E057152/1
• 负责人: Rodrigo Quian Quiroga
• 金额: $ 1.76万
• 依托单位: University of Leicester
• 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%2FE057152%2F1
• 关键词: Data; Reduction; Techniques; Systematic; Information

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 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，大脑的功能主要取决于位于不同大脑区域的大量不同神经元群体之间的相互作用。为了从经验上测试这些神经元群如何协同工作以产生感觉和感知等功能，神经科学家同时记录大脑中不同神经元群的活动。记录这一活动是实现了一些不同的方法。多电极已经成为研究特定大脑区域或不同区域中多个神经元同时活动的标准工具。编码在锋电位序列中的刺激信息是神经科学研究的主要焦点，并且经常根据各种反应或特征进行检查，例如锋电位计数、平均反应时间、第一锋电位潜伏期以及锋电位间期和放电率。在越来越多的实验中记录的大量反应数据阵列对数据分析以及神经元功能的解释和建模提出了许多挑战。在这项可行性研究中，我们建议将系统工程，数据简化技术和多元统计中的信息理论专家聚集在一起，以提供一种方法来最有效地分析大型神经种群的信息内容。特别地，我们把锋电位序列看作是一个信息编码和解码的过程，提出在信息论框架下，利用小波、伊卡和拓扑映射对锋电位序列中包含的信息特征进行提取、分类、量化和组织;并且我们建议使用自组织数据简化技术来改进互信息的估计，并探索多种不同的方法，以提供适合于不同性质的脑信号的组合分析的信息论方法。

项目成果

Latency and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe.

单神经元的潜伏期和选择性表示人体内侧颞叶中的分层处理。

• DOI: 10.1523/jneurosci.1640-08.2008
• 发表时间: 2008-09-03
• 期刊: The Journal of neuroscience : the official journal of the Society for Neuroscience
• 作者: Mormann F;Kornblith S;Quiroga RQ;Kraskov A;Cerf M;Fried I;Koch C
• 通讯作者: Koch C

Las neuronas de la conciencia

科学神经元

• 发表时间: 2008
• 期刊: Ciencia Cognitiva
• 作者: R Quian Quiroga