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.

记录体内和体外细胞外电极的神经活动时尖峰排序是必需的且非常重要的处理步骤，可用于识别单个神经元的活动。尖峰排序是一个复杂的算法过程，近年来，许多小组尝试解决此问题，从而产生了许多方法和软件包。但是，尖峰分选技术的验证很复杂。这是一个固有的无监督问题，很难找到通用指标来评估性能。结合细胞外和膜片钳或近细胞技术的同时记录可提供真实的数据，以评估穗分选方法。但是，它们的实用性受到限制，因为只能同时测量几个单元格。模拟的真实记录可以提供强大的替代手段来对尖峰分拣机的性能进行排名。MEArec，一种基于Python的软件，可以灵活，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，并可以复制各种有问题的方面进行尖峰排序，例如爆发，时空重叠事件和漂移。我们期望MEArec将为尖峰排序的开发和评估提供一个通用的测试平台，其中尖峰排序的开发人员可以快速生成和评估其算法的性能。