title: Existence of high-order correlations in cortical activity.
abstract: Neurons collect signals originating from a large number of other cells. The variability of this integrated population activity at the millisecond time scale is a critical constraint on the degree of signal integration and processing performed by single neurons. Optical imaging, EEG, and fMRI studies have indicated that cortical activity shows a high degree of variability at a time scale of hundreds of ms. However, currently no experimental methods are available to directly assess the variability in the activity of populations of neurons at a time scale closer to that of the characteristic time constants of neurons, i.e., around 10 ms. Here we integrate pertinent experimental data in one rigorous mathematical framework to demonstrate that (1) the high temporal variability in the spiking activity of individual neurons, (2) the second-order correlation properties of the spiking activity of cortical neurons, and (3) the correlations of the subthreshold dynamics, all impose high amplitude, fast variability in the population activity of cortical neurons. This implies that higher order correlations, a necessary condition for temporal coding models, must be a central feature of cortical dynamics.
authors.affiliation: Institute of Neuroinformatics University & ETH Zürich, Winterthurerstrasse 190, 8057 Zürich, Switzerland. ben@ini.phys.ethz.ch
authors.forename: Andrea
authors.initials: A
authors.lastname: Benucci
authors.forename: Paul F M J
authors.initials: PF
authors.lastname: Verschure
authors.forename: Peter
authors.initials: P
authors.lastname: König
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000222:Adaptation, Physiological / Q000502:physiology; D000818:Animals; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic