title: Analysis of multidimensional neural activity via CNN-UM.
abstract: In this paper we show that the Cellular Nonlinear Network Universal Machine (CNN-UM) is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: first, the occurrences of different patterns are counted, then the statistical significance of each occurrence frequency is calculated separately.
authors.affiliation: Analogical and Neural Computing Laboratory, Hungarian Academy of Sciences Computer and Automation Research Institute, Lágymányosi u. 13, Budapest, H-1111, Hungary. gal@sztaki.hu
authors.forename: Viktor
authors.initials: V
authors.lastname: Gál
authors.forename: Sonja
authors.initials: S
authors.lastname: Grün
authors.forename: Ronald
authors.initials: R
authors.lastname: Tetzlaff
mesh_terms: D000200:Action Potentials / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D013223:Statistics as Topic