SYSTEM FOR THE DETECTION OF NEURAL ENSEMBLES

神经网络集成检测系统

• 批准号: 6538899
• 负责人: THOMAS J MANUCCIA
• 金额: $ 36.07万
• 依托单位: SCHAFER CORPORAGIONT
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-06-22 至 2003-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6538899
• 关键词: Malacostraca; computer program /software; computer simulation; computer system design /evaluation; data collection methodology /evaluation; electronic recording system; electrophysiology; neural transmission; neurons; neurophysiology; time resolved data

DESCRIPTION(adapted from applicant's abstract): Current technology can simultaneously monitor the firing of approximately 150 neurons. The corresponding cross correlation matrix has well over 20,0000 independent entries, generally distributed in a seemingly random fashion because of the complex interconnections within typical neural tissue. The trend in experimental neurophysiology is clearly towards even larger sets of data. We propose to develop software for multineuron extracellular electrophysiology recording that will provide experimentalists with a near real-time display of the correlation in firing within such large groups of neurons. The device will be placed in the typical laboratory equipment chain after signal amplification, conditioning, and spike sorting. It will calculate (in near real-time) the correlation between all pairs of input spike trains, and then present a frequently updated display of the cross correlation matrix in such a way that highly correlated sub-sets of neurons within the recorded group will be displayed prominently. The device will be used to study changes in composition and correlation levels in these subsets as experimental conditions are changed, as an experimental animal is trained, and to help guide the placement of the recording electrode. PROPOSED COMMERCIAL APPLICATION: NOT AVAILABLE

描述（根据申请人的摘要改编）：当前的技术可以 同时监测大约150个神经元的发射。这 相应的跨相关矩阵具有超过20,0000多个独立的 条目，通常以看似随机的方式分布，因为 典型神经组织中的复杂互连。趋势中的 实验性神经生理学显然是针对更大的数据集。我们 建议开发用于多神经元外电生理学的软件 录音将为实验者提供近乎实时的展示 在如此大的神经元内发射的相关性。设备将 信号放大后，放置在典型的实验室设备链中， 调理和尖峰排序。它将（几乎实时计算） 所有成对的输入尖峰火车之间的相关性，然后呈现一个 经常更新跨相关矩阵的显示方式 记录组中神经元的高度相关子集将是 突出显示。该设备将用于研究组成的变化 随着实验条件的改变，这些子集中的相关水平， 作为实验动物的训练，并有助于指导放置 记录电极。 拟议的商业应用程序：不可用