Copy of UK Spike Train Analysis Task Force

英国 Spike Train 分析工作组的副本

• 批准号: EP/D077109/1
• 负责人: Stuart Baker
• 金额: $ 5.93万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD077109%2F1
• 关键词: Copy; UK; Spike; Train; Analysis

The computation that takes place in networks of brain cells (neurones) is more powerful that man-made computing, novel, and poorly understood. Neurophysiology is the science of extracting this computational data flow from these cell networks, and analysing it in order to understand the features of the information processing that is taking place.Recent advances in data acquisition have allowed neurophysiologists to record from networks of cells, as opposed to single cells, or cell pairs. The data that emanates provides a binary description of the information flow in the network, in the form of a series of single-point data streams that are recorded at spatial intervals.To understand the computational processes that this data describes, it is necessary to develop new mathematical and statistical techniques to correlate phenomena that occur in multiple streams against imposed stimulti, to generate additional insight by inferring something of the structure of the network producing the data, and to integrate discrete mathematical technqiues that address the research challenge (which is essentially to understand a high-paramater analogue process) at very small levels of scale.At present as a result of the difficulty of the problem, and the way in which the community currently works, there is a severe shortfall in robust mathematical techniques that can advance the proposed research challenge for networks of more than a handful of cells.The UK Spike Train Analysis Task Force will reverse this shortfall by providing an offline and online forum that will allow a newly consolidated expert community to develop mathematics in an a collaborative way.

发生在脑细胞(神经元)网络中的计算比人工计算更强大，新颖，但人们对此知之甚少。神经生理学是从这些细胞网络中提取这种计算数据流，并对其进行分析以了解正在发生的信息处理的特征的科学。数据获取方面的最新进展使神经生理学家能够从细胞网络而不是单个细胞或细胞对进行记录。所发出的数据以在空间间隔处记录的一系列单点数据流的形式提供了网络中信息流的二进制描述。为了理解该数据描述的计算过程，有必要开发新的数学和统计技术以将在多个流中发生的现象与施加的多个流相关联，通过推断产生数据的网络结构的某些东西来产生额外的洞察力，并且在非常小的尺度级别上集成解决研究挑战(本质上是理解高参数模拟过程)的离散数学技术。由于该问题的困难，以社区目前的工作方式，在能够推进由多个细胞组成的网络的拟议研究挑战的强大数学技术方面存在严重不足。英国尖峰列车分析任务组将通过提供一个线下和在线论坛来弥补这一不足，该论坛将允许一个新整合的专家社区以合作的方式发展数学。