Extracellular recordings with multi-electrode arrays is one of the basic tools of contemporary neuroscience. These recordings are mostly used to monitor the activities, understood as sequences of emitted action potentials, of many individual neurons. But the raw data produced by extracellular recordings are most commonly a mixture of activities from several neurons. In order to get the activities of the individual contributing neurons, a pre-processing step called spike sorting is required. We present here a pure Python implementation of a well tested spike sorting procedure. The latter was designed in a modular way in order to favour a smooth transition from an interactive sorting, for instance with IPython, to an automatic one. Surprisingly enough - or sadly enough, depending on one's view point -, recoding our now 15 years old procedure into Python was the occasion of major methodological improvements.
翻译:多电子阳极阵列的外细胞记录是当代神经科学的基本工具之一。 这些记录大多用于监测许多单个神经元的活动,被理解为排放行动潜力的序列。 但外细胞记录产生的原始数据通常是多个神经元活动的混合体。 为了获得个别贡献神经元的活动,需要有一个叫作钉钉子排序的预处理步骤。 我们在这里展示了一个经过充分测试的峰值排序程序的纯 Python 实施过程。 后者是模块化的, 目的是有利于从交互式排序( 例如IPython ) 向自动排序的平稳过渡。 令人惊讶的是, 或悲哀的是, 足够多的, 取决于一个人的视角 — 将我们15年前的程序重新编码为Python 是方法上重大改进的时机。