Efficient identification of assembly neurons within massively parallel spike trains.
The chance of detecting assembly activity is expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. Although such massively parallel recordings are now becoming available, methods able to analyze such data for spike correlation are still rare, as a combinatorial explosion often makes it infeasible to extend methods developed for smaller data sets. By evaluating pattern complexity distributions the existence of correlated groups can be detected, but their member neurons cannot be identified. In this contribution, we present approaches to actually identify the individual neurons involved in assemblies. Our results may complement other methods and also provide a way to reduce data sets to the "relevant" neurons, thus allowing us to carry out a refined analysis of the detailed correlation structure due to reduced computation time.
Berger
Denise
D
Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, 10115 Berlin, Germany.
Borgelt
Christian
C
Louis
Sebastien
S
Morrison
Abigail
A
Grün
Sonja
S
D000200:Action Potentials; D000818:Animals; D002415:Cats; D001330:Electronic Data Processing / Q000379:methods*; D004594:Electrophysiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000166:cytology / Q000502:physiology*; D014793:Visual Cortex / Q000166:cytology