title: ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains.
abstract: 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.
authors.affiliation: Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany.
authors.forename: Emiliano
authors.identifier: 0000-0001-8263-9278
authors.initials: E
authors.lastname: Torre
authors.affiliation: Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany.
authors.forename: Carlos
authors.initials: C
authors.lastname: Canova
authors.affiliation: Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Denker
authors.affiliation: Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: George
authors.initials: G
authors.lastname: Gerstein
authors.affiliation: Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany.|Department of Physics, RWTH Aachen University, Aachen, Germany.
authors.forename: Moritz
authors.initials: M
authors.lastname: Helias
authors.affiliation: Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany.|Department of Biology, RWTH Aachen University, Aachen, Germany.
authors.forename: Sonja
authors.identifier: 0000-0003-2829-2220
authors.initials: S
authors.lastname: Grün
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology