Live spike sorting for multichannel and high-channel recordings

针对多通道和高通道录音的实时尖峰排序

• 批准号: 10759767
• 负责人: Achim Klug
• 金额: $ 43.85万
• 依托单位: POPNEURON LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-22 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10759767
• 关键词: Action Potentials; Algorithms; Animal Experiments; Area; Benchmarking; Brain; Cells; Complement; Computational Technique; Computers; Data; Data Set; Devices; Electrodes; Equipment; Experimental Designs; Feedback; Goals; Graph; Libraries; Logic; Marketing; Measures; Memory; Neurons; Outcome; Pattern; Positioning Attribute; Process; Pythons; Quality Control; Research Personnel; Running; Slave; Sorting; Speed; Stream; System; Technology; Time; Willow; Work; complex data; data reduction; data standards; data streams; design; experimental study; extracellular; high end computer; in vivo; innovation; laboratory equipment; mathematical methods; neural; neurophysiology; novel; prototype; time use; virtual

Project Summary: The goal of this project is to create two prototypes of a novel live spike sorting system which can be used by investigators to spike sort streams of neural data recorded by multi-channel, high channel and ultra-high channel probes. In most in-vivo extracellular recording conditions, an electrode can pick up neural spikes from several nearby neurons resulting in so-called “multi-unit” activity in the recording trace. Spike sorting algorithms are then used to separate this multi-unit activity into several sets of “single-unit” activities, each of which represents the action potential firing pattern of a single neuron. This sorting process is typically a computationally intensive process and is growing into a critical technology gap with the advent of multi and high channel count hardware. Live spike sorting of a complete set of multichannel data has been challenging if not impossible. On the other hand, there is a demand for live spike sorting during an experiment, especially by those investigators who record from functionally heterogenous brain areas such as, for example, all cortical regions. If an investigator had the ability to review live single cell data, he/she could determine the quality of the data and adjust the electrode position or decide on next experimental steps based on the incoming results. We recently developed the GEMsort algorithm, which, compared to existing spike sorting algorithms, was designed to sort neural spikes from multichannel probes with immediate sorting outcomes. These algorithms provide powerful, accurate yet computationally inexpensive spike sorting due to a different mathematical approach. As a result, these algorithms can spike sort complete streams of complex data, including data recorded with high channel and ultra-high channel electrodes virtually in real time. In this proposal, we will develop two tabletop-sized systems based on Field-Programmable Gate Array (FPGA) technology for laboratory use. These systems will be based on the GEMsort algorithm and add live spike sorting capabilities to an investigator's existing recording setup.

项目概要： 该项目的目标是创建两个新型活穗分拣系统的原型，可供以下人员使用 研究人员对多通道、高通道和超高通道记录的神经数据流进行了尖峰排序， 通道探针。在大多数体内细胞外记录条件下，电极可以拾取神经尖峰 从几个附近的神经元，导致所谓的“多单位”活动的记录痕迹。尖峰分选 然后使用算法将多单元活动分成几组“单单元”活动， 其中的一个表示单个神经元的动作电位放电模式。这个排序过程通常是 计算密集型的过程，并正在成长为一个关键的技术差距，随着多和 高通道数硬件。完整多通道数据集的实时尖峰排序一直具有挑战性 如果不是不可能话。另一方面，在实验过程中需要活尖峰分选， 特别是那些从功能异质的大脑区域记录的研究者， 例如，所有皮层区域。如果研究者有能力审查活单细胞数据，他/她可以 确定数据的质量并调整电极位置或决定下一个实验步骤 根据结果。 我们最近开发了GEMSort算法，与现有的尖峰排序算法相比， 设计用于对来自多通道探针的神经尖峰进行分类，并立即产生分类结果。这些 算法提供了强大的，准确的，但计算成本低廉的尖峰排序，由于不同的 数学方法因此，这些算法可以对复杂数据的完整流进行尖峰排序， 包括利用高通道和超高通道电极虚拟地以真实的时间记录的数据。在这 根据该方案，我们将开发两个基于现场可编程门阵列（FPGA）的桌面大小的系统 实验室使用的技术。这些系统将在GEMSort算法的基础上添加活穗 分类能力到调查员的现有记录设置。