Picado Muiño David, Borgelt Christian
European Centre for Soft Computing, Edificio Científico Tecnológico, Gonzalo Gutiérrez Quirós, s/n, 33600 Mieres, Spain.
Comput Intell Neurosci. 2015;2015:427829. doi: 10.1155/2015/427829. Epub 2015 Mar 8.
In recent years numerous improvements have been made in multiple-electrode recordings (i.e., parallel spike-train recordings) and spike sorting to the extent that nowadays it is possible to monitor the activity of up to hundreds of neurons simultaneously. Due to these improvements it is now potentially possible to identify assembly activity (roughly understood as significant synchronous spiking of a group of neurons) from these recordings, which-if it can be demonstrated reliably-would significantly improve our understanding of neural activity and neural coding. However, several methodological problems remain when trying to do so and, among them, a principal one is the combinatorial explosion that one faces when considering all potential neuronal assemblies, since in principle every subset of the recorded neurons constitutes a candidate set for an assembly. We present several statistical tests to identify assembly neurons (i.e., neurons that participate in a neuronal assembly) from parallel spike trains with the aim of reducing the set of neurons to a relevant subset of them and this way ease the task of identifying neuronal assemblies in further analyses. These tests are an improvement of those introduced in the work by Berger et al. (2010) based on additional features like spike weight or pairwise overlap and on alternative ways to identify spike coincidences (e.g., by avoiding time binning, which tends to lose information).
近年来，多电极记录（即并行尖峰序列记录）和尖峰分类技术有了诸多改进，如今已能够同时监测多达数百个神经元的活动。由于这些改进，现在有可能从这些记录中识别集合活动（大致理解为一组神经元的显著同步尖峰活动），如果能够可靠地证明这一点，将显著增进我们对神经活动和神经编码的理解。然而，在尝试这样做时仍存在几个方法学问题，其中一个主要问题是在考虑所有潜在的神经元集合时面临的组合爆炸，因为原则上记录的神经元的每个子集都构成一个集合的候选集。我们提出了几种统计测试，用于从并行尖峰序列中识别集合神经元（即参与神经元集合的神经元），目的是将神经元集合减少到相关的子集中，从而在进一步分析中简化识别神经元集合的任务。这些测试是在Berger等人（2010年）的工作基础上进行的改进，基于尖峰权重或成对重叠等附加特征以及识别尖峰重合的替代方法（例如，通过避免时间分箱，因为时间分箱往往会丢失信息）。
