Live spike sorting for multichannel and high-channel recordings

项目总结： 该项目的目标是创建两个新的活体钉子分类系统的原型，可以用于 调查人员将对多通道、高通道和超高记录的神经数据流进行分类 航道探测器。在大多数活体细胞外记录条件下，电极可以拾取神经尖峰。 来自附近的几个神经元，导致在记录痕迹中出现所谓的“多单位”活动。穗状物分类 然后使用算法将这种多单元活动分成几组“单单元”活动，每组 它代表了单个神经元的动作电位放电模式。此排序过程通常是一个 这是一个计算密集型的过程，并且随着多个和 高通道数硬件。对一组完整的多通道数据进行实时峰值排序一直是一个挑战 如果不是不可能的话。另一方面，在实验期间存在对活体穗分类的需求， 尤其是那些从功能不同的大脑区域进行记录的研究人员，例如， 例如，所有皮质区域。如果调查员有能力查看活的单细胞数据，他/她就可以 确定数据质量并调整电极位置或决定下一步实验步骤 根据收到的结果。 我们最近开发了GEM排序算法，与现有的尖峰排序算法相比， 设计用于从多通道探头中对神经尖峰信号进行分类，并立即产生分类结果。这些 算法提供了强大、准确但计算成本低的尖峰排序，这是由于不同的 数学方法。因此，这些算法可以对复杂数据流的完整流进行尖峰排序， 包括用高通道和超高通道电极虚拟实时记录的数据。在这 建议，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。这些系统将基于GEM排序算法并添加实时尖峰 将分类功能添加到调查员现有的录音设置。

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.