title: Reconstruction of network structures from repeating spike patterns in simulated bursting dynamics.
abstract: Repeating patterns of spike sequences from a neuronal network have been proposed to be useful in the reconstruction of the network topology. Reverberations in a physiologically realistic model with various physical connection topologies (from random to scale free) have been simulated to study the effectiveness of the pattern-matching method in the reconstruction of network topology from network dynamics. Simulation results show that functional networks reconstructed from repeating spike patterns can be quite different from the original physical networks; even global properties, such as the degree distribution, cannot always be recovered. However, the pattern-matching method can be effective in identifying hubs in the network. Since the form of reverberations is quite different for networks with and without hubs, the form of reverberations together with the reconstruction by repeating spike patterns might provide a reliable method to detect hubs in neuronal cultures.
authors.affiliation: Institute of Physics, Academia Sinica, Nankang, Taipei, Taiwan 115, Republic of China.
authors.forename: Hao
authors.initials: H
authors.lastname: Song
authors.affiliation: Institute of Physics, Academia Sinica, Nankang, Taipei, Taiwan 115, Republic of China.
authors.forename: Chun-Chung
authors.initials: CC
authors.lastname: Chen
authors.affiliation: Neuro-Electronics Research Flanders, Kapeldreef 75, 3001 Leuven, Belgium.
authors.forename: Jyh-Jang
authors.initials: JJ
authors.lastname: Sun
authors.affiliation: Institute of Physics, Academia Sinica, Nankang, Taipei, Taiwan 115, Republic of China and Department of Physics and Center for Complex Systems, National Central University, Chungli, Taiwan 320, Republic of China.
authors.forename: Pik-Yin
authors.initials: PY
authors.lastname: Lai
authors.affiliation: Institute of Physics, Academia Sinica, Nankang, Taipei, Taiwan 115, Republic of China and Department of Physics and Center for Complex Systems, National Central University, Chungli, Taiwan 320, Republic of China.
authors.forename: C K
authors.initials: CK
authors.lastname: Chan
mesh_terms: D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology* / Q000502:physiology; D009474:Neurons / Q000166:cytology*