ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

PLoS Comput Biol. 2016 Jul 15;12(7):e1004939. doi: 10.1371/journal.pcbi.1004939. eCollection 2016 Jul.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials / physiology*
  • Algorithms
  • Cerebral Cortex / cytology
  • Cerebral Cortex / physiology
  • Computational Biology / methods*
  • Humans
  • Models, Neurological*
  • Neurons / physiology

Associated data

  • Dryad/10.5061/dryad.cb360

Grant support

This work was partly supported by the Helmholtz Portfolio Supercomputing and Modeling for the Human Brain (SMHB, []), the Helmholtz young investigator group VH-NG-1028 [], the EU Grant 269921 (BrainScaleS []), and the EU Grant 604102 (Human Brain Project, HBP []), the Priority Program SPP 1665 of the DFG (GR 1753/4-1 and DE 2175/1-1) and the Ger-Jpn Comput Neurosci Project BMBF Grant 01GQ1114. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.