DEVELOPMENT OF AN AUTOMATED NEURAL SPIKE DISCRIMINATOR

随着细胞外多通道的可用性不断增加 微电极阵列和潜在的大量同时 神经系统研究中的记录、数据处理能力和 容量是非常重要的。一种自动化的在线神经棘波分离， 分类数据收集和分析系统必须在以下方面取得平衡 性能、速度和人为干预。 这项工作的长期目标是想出一个商业广告 用于识别神经网络拓扑的自动化系统。这个 这项建议的目标是开发一种自动化的多渠道 神经放电在线识别的硬件子系统。在这 子系统，一种使用Haar变换基的算法。该算法 将完全自动化，从学习尖峰波形到分类 尖峰，并扩展到硬件上的多通道多单元识别。 该系统将在体内实验中进行测试。 多通道识别和分析系统的开发已经 对传统神经生理学产生巨大影响的潜力， 生物与计算神经网络研究与神经控制 假肢装置。

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.