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.

项目摘要： 该项目的目的是创建两个新颖的现场尖峰分类系统的原型，可以由 多渠道，高通道和超高的研究人员的神经数据流尖峰流数流 通道探针。在大多数体内的细胞外记录条件下，电极可以拾取神经尖峰 来自附近的几个神经元，导致记录痕迹中所谓的“多单元”活动。尖峰分类 然后，使用算法将此多单元活动分为几组“单单元”活动，每个活动 其中代表单个神经元的动作电势触发模式。这个分类过程通常是 在计算密集的过程中，随着Multi和Multi和 高通道计数硬件。一组完整的多通道数据的现场尖峰排序已受到挑战 如果不是不可能的话。另一方面，在实验过程中需要实时尖峰分类， 特别是那些记录功能异体大脑区域（例如 例如，所有皮质区域。如果调查员有能力审查实时单元数据，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 基于传入结果。 我们最近开发了Gemsort算法，该算法与现有的Spike分类算法相比 旨在从多通道问题中对神经峰进行分类，并立即分类结果。这些 由于不同的 数学方法。结果，这些算法可以峰值排序完整的复杂数据流， 包括使用高通道和超高通道电极的数据实际上实时实时。在这个 提案，我们将基于现场可编程门阵列（FPGA）开发两个桌面大小的系统 实验室使用技术。这些系统将基于Gemsort算法并添加实时峰值 为调查员现有的录音设置分类功能。