title: Complexity distribution as a measure for assembly size and temporal precision.
abstract: The efficient detection of higher-order synchronization in massively parallel data is of great importance in understanding computational processes in the cortex and represents a significant statistical challenge. To overcome the combinatorial explosion of different spike patterns taking place as the number of neurons increases, a method based on population measures would prove very useful. Following previous work in this direction, we examine the distribution of spike counts across neurons per time bin ('complexity distribution') and devise a method to reliably extract the size and temporal precision of synchronous groups of neurons, even in the presence of strong rate covariations.
authors.affiliation: RIKEN Brain Science Institute, Wako-Shi, Japan.
authors.forename: Sebastien
authors.initials: S
authors.lastname: Louis
authors.forename: Christian
authors.initials: C
authors.lastname: Borgelt
authors.forename: Sonja
authors.initials: S
authors.lastname: Grün
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology; D003198:Computer Simulation; D004594:Electrophysiology / Q000379:methods* / Q000639:trends; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000502:physiology; D009482:Neurophysiology / Q000379:methods* / Q000639:trends; D011897:Random Allocation; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors