DEVELOPMENT OF AN AUTOMATED NEURAL SPIKE DISCRIMINATOR

自动神经尖峰鉴别器的开发

• 批准号: 3504570
• 负责人: XIAOWEI YANG
• 金额: $ 5万
• 依托单位: MULITCHANNEL CONCEPTS, INC.
• 依托单位国家: 美国
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-05-01 至 1991-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3504570
• 关键词: biomedical automation; biomedical equipment development; electrophysiology; membrane channels; microelectrodes; neurophysiology; neurosciences; online computer

With the increasing availability of multichannel extracellular microelectrode arrays and the potentially large number of simultaneous recordings in neural system studies, data processing capability and capacity are very important. An automated on-line neural spike separation, sorted data collection and analysis system must strike a balance between performance, speed, and human intervention. The long-term objective of this work is to come up with a commercial automated system for identification of neural network topology. The objective of this proposal is to develop an automated multi-channel hardware subsystem for discrimination of neural spikes on-line. In this subsystem, an algorithm using the Haar transform basis. This algorithm will be totally automated from learning spike waveforms to classifying spikes and extended to multi-channel multiunit discrimination in hardware. The system will be tested in in vivo experiments. The development of a multi-channel discrimination and analysis system has the potential to make a great impact on traditional neurophysiology, biological and computational neural network research and neural control of prosthetic devices.

随着多通道细胞外 微电极阵列和潜在的大量的同时 神经系统研究中的记录，数据处理能力和 能力非常重要。 一种自动化的在线神经锋电位分离， 分类数据收集和分析系统必须在以下两个方面取得平衡 性能、速度和人工干预。 这项工作的长期目标是提出一个商业 用于识别神经网络拓扑结构的自动化系统。 的 该提案的目的是开发一种自动化的多通道 用于在线鉴别神经尖峰的硬件子系统。 在这 子系统，一个算法使用哈尔变换的基础。 该算法 从学习尖峰波形到分类， 并扩展到硬件中的多通道多单元鉴别。 该系统将在体内实验中进行测试。 多通道鉴别分析系统的研制， 对传统神经生理学产生巨大影响的潜力， 生物和计算神经网络研究和神经控制 假肢装置