Mining Large-Scale Neural Ensemble Recordings

挖掘大规模神经集成记录

• 批准号: 7753635
• 负责人: Karim G Oweiss
• 金额: $ 31.55万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-15 至 2011-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7753635
• 关键词: Algorithms; Archives; Behavioral; Benchmarking; Brain; Communities; Companions; Complex; Computer software; Data; Data Analyses; Data Set; Development; Devices; Discipline; Electronics; Engineering; Environment; Event; Information Retrieval; Licensing; Maps; Measures; Microelectrodes; Microfabrication; Mining; Modeling; Modification; Neurobiology; Neurons; Neurosciences; Performance; Phase; Physiological; Plug-in; Population; Process; Public Domains; Research; Research Activity; Research Personnel; Simulate; Software Tools; Sorting - Cell Movement; Stimulus; Structure; Techniques; Technology; Testing; Training; computerized data processing; data format; data mining; data sharing; density; design; extracellular; graphical user interface; improved; novel; open source; programs; quantum; relating to nervous system; research study; simulation; software development; theories; tool; user-friendly

Understanding how neurons act in concert requires observation of the collective activity of large, spatially distributed neuronal aggregates. Largely motivated by the rapid advances in microfabrication technology, high-density implantable electronic interfaces are now enabling the acquisition of large volumes of physiological and behavioral data, triggering concomitant neurobiological discoveries. Nevertheless, advances in the fabrication of high-density microelectrode arrays (MEAs) were not associated with quantum advances in array processing and data analysis techniques in order to unveil the affluent information content in the recorded neural data. As the number of recording channels on a single microprobe device becomes astoundingly large, no discipline is more challenged than signal processing and data mining in accommodating these new advances within the emerging neural engineering arena. There is an intrinsic need to design new algorithms and software tools to optimize array processing and information retrieval from multiple spike train neural data to answer several persistent neuroscience questions. The fundamental objective of this research is to explore and develop an integrated array processing and data mining framework with companion software tools to extract the useful information from large-scale neuronal ensemble recordings through the following aims: 1. Develop scalable and adaptive array processing algorithms for processing high-density microelectrode array recordings in short and long-term experimental setups; 2. Develop data analysis and clustering techniques for mining functional interdependency among neural ensembles from the recorded mixtures; 3. Develop an open source software package that integrates the array processing algorithms developed under aim 1 with the data clustering algorithms developed under aim 2 and disseminate the package to the community; 4. Test and demonstrate the efficiency of these techniques and the integrity of the developed software on simulated and experimental data shared by investigators in the field. Upon completion of the proposed research activity, we anticipate to provide numerous users in the neuroscience community with novel tools for processing and analyzing their data with increased accuracy, maximized efficiency and sustained reliability in their behavioral experiments.

了解神经元如何协同行动需要观察大型空间的集体活动 分布式神经元聚集体。很大程度上受到微加工技术快速进步的推动， 高密度植入式电子接口现在能够采集大量的 生理和行为数据，引发相应的神经生物学发现。尽管如此， 高密度微电极阵列（MEA）制造的进步与量子无关 阵列处理和数据分析技术的进步，以揭示丰富的信息内容 在记录的神经数据中。随着单个微探针设备上的记录通道数量变得 规模惊人，没有哪个学科比信号处理和数据挖掘更具挑战性。 在新兴的神经工程领域适应这些新进展。有一个内在的 需要设计新的算法和软件工具来优化阵列处理和信息检索 多个尖峰训练神经数据来回答几个持续存在的神经科学问题。 本研究的根本目标是探索和开发集成的阵列处理和 具有配套软件工具的数据挖掘框架，可从大规模数据中提取有用信息 通过以下目标进行神经元集成记录： 1. 开发可扩展、自适应的阵列处理算法，用于处理高密度微电极 短期和长期实验设置中的阵列记录； 2. 开发数据分析和聚类技术，用于挖掘神经网络之间的功能相互依赖性 来自记录的混合物的合奏； 3. 开发一个开源软件包，集成所开发的阵列处理算法 根据目标 1，使用根据目标 2 开发的数据聚类算法，并将该软件包分发给 社区; 4. 测试并证明这些技术的效率以及所开发软件的完整性 现场研究人员共享的模拟和实验数据。 完成拟议的研究活动后，我们预计将为大量用户提供 神经科学界拥有新的工具来更准确地处理和分析数据， 最大限度地提高行为实验的效率和持续的可靠性。