PDF(Pubmed)
Abstract：
Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.
摘要：
在多天中准确跟踪相同的神经元对于研究学习和适应过程中神经元活动的变化至关重要。高密度细胞外电生理记录探针的研究进展,比如神经像素，提供了一个有希望的途径来实现这一目标。在多个记录中识别相同的神经元是，然而,由于组织相对于记录部位的非刚性运动（漂移）和来自某些神经元的信号丢失而复杂化。这里,我们提出了一种神经元跟踪方法，可以独立于放电统计来识别相同的细胞，大多数现有方法使用的。我们的方法基于尖峰排序簇的日间非刚性对齐。我们使用测量的视觉感受野在小鼠中验证了相同的细胞身份。此方法在1到47天之间的数据集上成功，平均回收率为84%。
