ASSET: analysis of sequences of synchronous events in massively parallel spike trains

With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated m...借助现代神经记录技术实现对大量神经元活动的同步观测后，识别参与协同信息处理的神经元子网络的可能性显著提升。同步脉冲事件（Synchronous Spike Events, SSEs）是这类协同脉冲活动的一类，其以极高的时间精度传导神经活动信号。同步脉冲链（synfire chain）被提出作为这类网络信息处理的潜在模型之一。既往研究提出了一种用于可视化大规模并行脉冲序列中SSEs的方法，该方法基于交集矩阵：矩阵的每个元素代表两个对应时间窗内活跃神经元的重叠程度。重复出现的SSEs会在矩阵中呈现为高重叠值的对角结构。然而该方法仍需依靠人工目视检查来识别这些对角结构，而非通过量化分析完成。本文提出了ASSET（Analysis of Sequences of Synchronous EvenTs），这是一种经过改进的全自动化……