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 method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity.Author Summary: Neurons in the cerebral cortex are highly interconnected. However, the mechanisms of coordinated processing in the neuronal network are not yet understood. Theoretical studies have proposed synchronized electrical impulses (spikes) propagating between groups of nerve cells as a basis of cortical processing. Indeed, animal studies provide experimental evidence that spike synchronization occurs in relation to behavior. However, the observation of sequences of synchronous activity has not been reported so far, presumably due to two fundamental problems. First, the long-standing lack of simultaneous recordings of large populations of neurons, which only recently were enabled by advances in recording technology. Second, the absence of proper tools required to find these activity patterns in such high-dimensional data. Addressing the second issue, we introduce here a fully automatized mathematical method that advances an existing visual approach to identify sequences of synchronous events in large data sets. We demonstrate the efficacy of our method on a range of simulated test data that capture the characteristics and variability of experimental data. Our tool will serve future studies in their search for spike time coordination at millisecond precision in the brain.