权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Mining Large-Scale Neural Ensemble Recordings

挖掘大规模神经集成记录

基本信息

批准号：
7340120
负责人：
Karim G Oweiss
金额：
$ 12.44万
依托单位：
MICHIGAN STATE UNIVERSITY
依托单位国家：
美国
项目类别：
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/7340120
关键词：
Algorithms Archives Behavioral Benchmarking Brain Cluster Analysis Communities Companions Complex Computer information processing Computer software Condition 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 Numbers 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 mining 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

项目摘要

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.

理解神经元如何协同工作需要观察大的、空间上的集体活动。分布的神经元聚集体。在微细加工技术快速发展的推动下，高密度的可植入电子接口现在能够采集大量的生理和行为数据，引发伴随的神经生物学发现。然而，尽管如此，高密度微电极阵列（MEA）制造的进展与量子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。随着单个微探针装置上的记录通道的数量变得越来越多，令人惊讶的大，没有学科比信号处理和数据挖掘更具有挑战性，在新兴的神经工程竞技场中适应这些新的进展。有着内在需要设计新的算法和软件工具，以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的神经科学问题。本研究的基本目标是探索和发展一种集成的阵列处理和数据挖掘框架与配套的软件工具，以提取有用的信息，从大规模的通过以下目的进行神经元整体记录： 1.开发可扩展和自适应的阵列处理算法，用于处理高密度微电极在短期和长期实验装置中的阵列记录; 2.开发数据分析和聚类技术，以挖掘神经元之间的功能相互依赖性从记录的混合物合奏; 3.开发一个开源软件包，集成所开发的阵列处理算法与根据目标2开发的数据聚类算法相结合，并将该软件包分发给社区; 4.测试和演示这些技术的效率和开发的软件的完整性，现场调查人员共享的模拟和实验数据。在拟议的研究活动完成后，我们预计将提供大量的用户在神经科学界拥有新颖的工具来处理和分析数据，提高准确性，最大化的效率和持续的可靠性在他们的行为实验。