Institut de la Vision, INSERM UMRS 968, UPMC UM 80, Paris, France.
Laboratoire de Physique Statistique, CNRS, ENS, UPMC, 75005, Paris, France.
Elife. 2018 Mar 20;7:e34518. doi: 10.7554/eLife.34518.
In recent years, multielectrode arrays and large silicon probes have been developed to record simultaneously between hundreds and thousands of electrodes packed with a high density. However, they require novel methods to extract the spiking activity of large ensembles of neurons. Here, we developed a new toolbox to sort spikes from these large-scale extracellular data. To validate our method, we performed simultaneous extracellular and loose patch recordings in rodents to obtain 'ground truth' data, where the solution to this sorting problem is known for one cell. The performance of our algorithm was always close to the best expected performance, over a broad range of signal-to-noise ratios, in vitro and in vivo. The algorithm is entirely parallelized and has been successfully tested on recordings with up to 4225 electrodes. Our toolbox thus offers a generic solution to sort accurately spikes for up to thousands of electrodes.
近年来，已经开发出多电极阵列和大型硅探针，以同时记录数百到数千个高密度封装的电极。然而，它们需要新的方法来提取大量神经元集群的尖峰活动。在这里，我们开发了一个新的工具箱来从这些大规模的细胞外数据中对尖峰进行分类。为了验证我们的方法，我们在啮齿动物中同时进行细胞外和松散贴附记录，以获得“真实数据”，其中已知一个细胞的这个分类问题的解决方案。我们的算法在体外和体内的广泛信噪比范围内，始终接近最佳预期性能。该算法完全并行化，并已成功应用于多达 4225 个电极的记录。因此，我们的工具箱为多达数千个电极的准确尖峰分类提供了通用的解决方案。
