Centre for Integrative Neuroplasticity (CINPLA), University of Oslo, Oslo, Norway.
Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland.
Neuroinformatics. 2021 Jan;19(1):185-204. doi: 10.1007/s12021-020-09467-7.
When recording neural activity from extracellular electrodes, both in vivo and in vitro, spike sorting is a required and very important processing step that allows for identification of single neurons' activity. Spike sorting is a complex algorithmic procedure, and in recent years many groups have attempted to tackle this problem, resulting in numerous methods and software packages. However, validation of spike sorting techniques is complicated. It is an inherently unsupervised problem and it is hard to find universal metrics to evaluate performance. Simultaneous recordings that combine extracellular and patch-clamp or juxtacellular techniques can provide ground-truth data to evaluate spike sorting methods. However, their utility is limited by the fact that only a few cells can be measured at the same time. Simulated ground-truth recordings can provide a powerful alternative mean to rank the performance of spike sorters. We present here MEArec, a Python-based software which permits flexible and fast simulation of extracellular recordings. MEArec allows users to generate extracellular signals on various customizable electrode designs and can replicate various problematic aspects for spike sorting, such as bursting, spatio-temporal overlapping events, and drifts. We expect MEArec will provide a common testbench for spike sorting development and evaluation, in which spike sorting developers can rapidly generate and evaluate the performance of their algorithms.
在记录细胞外电极的神经活动时，无论是在体还是在体，尖峰分选都是一个必需的非常重要的处理步骤，它允许识别单个神经元的活动。尖峰分选是一个复杂的算法过程，近年来，许多研究小组都试图解决这个问题，提出了许多方法和软件包。然而，尖峰分选技术的验证很复杂。这是一个本质上无监督的问题，很难找到通用的指标来评估性能。结合细胞外和膜片钳或细胞内技术的同时记录可以提供地面真实数据来评估尖峰分选方法。然而，它们的实用性受到只能同时测量少数细胞的事实的限制。模拟地面真实记录可以提供一种强大的替代方法来对尖峰分选器的性能进行排名。我们在这里介绍 MEArec，这是一个基于 Python 的软件，允许灵活快速地模拟细胞外记录。MEArec 允许用户在各种可定制的电极设计上生成细胞外信号，并可以复制尖峰分选的各种有问题的方面，如爆发、时空重叠事件和漂移。我们预计 MEArec 将为尖峰分选的开发和评估提供一个通用的测试平台，在这个平台上，尖峰分选开发人员可以快速生成和评估他们算法的性能。
