Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution.
Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution.
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DOI：
10.1007/s10827-009-0154-6
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发表时间：
2010-08
影响因子：
1.2
通讯作者：
Battaglia FP
中科院分区：
文献类型：
--
作者：
Peyrache A;Benchenane K;Khamassi M;Wiener SI;Battaglia FP
许多单个神经元的同时记录揭示了从单个尖峰到全局振荡的时间尺度的网络处理的独特见解。神经元动态地自我组织成被称为细胞集合的共激活元素的子组。此外，这些细胞集合体被重新激活或重放，优先在随后的休息或睡眠期间，这是记忆痕迹巩固的一种建议机制。在这里，我们采用主成分分析来隔离这种模式的神经活动。此外，一个措施来量化的模板模式的瞬时活动的相似性，我们推导出理论分布的零假设之间没有相关性的尖峰列车，允许一个瞬时coactivations的统计意义进行评估。因此，当在与识别模式的时期不同的时期（例如，随后的睡眠）中应用时，该测量允许识别再激活的时间和强度。该测量的分布提供了关于再激活事件的动态的信息：在睡眠中，这些事件作为瞬变而不是作为连续过程发生。
Simultaneous recordings of many single neurons reveals unique insights into network processing spanning the timescale from single spikes to global oscillations. Neurons dynamically self-organize in subgroups of coactivated elements referred to as cell assemblies. Furthermore, these cell assemblies are reactivated, or replayed, preferentially during subsequent rest or sleep episodes, a proposed mechanism for memory trace consolidation. Here we employ Principal Component Analysis to isolate such patterns of neural activity. In addition, a measure is developed to quantify the similarity of instantaneous activity with a template pattern, and we derive theoretical distributions for the null hypothesis of no correlation between spike trains, allowing one to evaluate the statistical significance of instantaneous coactivations. Hence, when applied in an epoch different from the one where the patterns were identified, (e.g. subsequent sleep) this measure allows to identify times and intensities of reactivation. The distribution of this measure provides information on the dynamics of reactivation events: in sleep these occur as transients rather than as a continuous process.
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影响因子：
1.9
作者：
OJA, E
通讯作者：
OJA, E
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