Mining Large-Scale Neural Ensemble Recordings

描述（由申请人提供）：了解神经元如何协同作用需要观察空间分布的大型神经元聚集体的集体活动。由于微制造技术的快速发展，高密度可植入电子接口现在能够获得大量的生理和行为数据，引发伴随的神经生物学发现。然而，高密度微电极阵列（MEA）制造的进步与阵列处理和数据分析技术的量子进步无关，以揭示记录的神经数据中丰富的信息内容。随着单个微探针设备上的记录通道数量变得惊人地大，在新兴的神经工程竞技场中，没有学科比信号处理和数据挖掘更具有挑战性。设计新的算法和软件工具来优化阵列处理和从多个尖峰序列神经数据的信息检索以回答几个持续的神经科学问题是一种内在的需求。本研究的基本目标是探索和开发一个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，从大规模的神经元系综记录通过以下目标：1。开发可扩展和自适应的阵列处理算法，用于在短期和长期实验设置中处理高密度微电极阵列记录; 2.开发数据分析和聚类技术，用于从记录的混合物中挖掘神经集合之间的功能相互依赖性; 3.开发一个开放源码软件包，将根据目标1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，并向社区分发该软件包; 4.测试和证明这些技术的效率和开发的软件的完整性，在该领域的研究人员共享的模拟和实验数据。在完成拟议的研究活动后，我们预计将为神经科学界的众多用户提供新的工具，用于处理和分析他们的数据，提高准确性，最大限度地提高效率和持续的可靠性，在他们的行为实验。

DESCRIPTION (provided by applicant): 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 micro fabrication 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.