Abstract
The human electroencephalogram (EEG) is globally characterized by a 1/f power spectrum superimposed with certain peaks, whereby the "alpha peak" in a frequency range of 8-14 Hz is the most prominent one for relaxed states of wakefulness. We present simulations of a minimal dynamical network model of leaky integrator neurons attached to the nodes of an evolving directed and weighted random graph (an Erdos-Renyi graph). We derive a model of the dendritic field potential (DFP) for the neurons leading to a simulated EEG that describes the global activity of the network. Depending on the network size, we find an oscillatory transition of the simulated EEG when the network reaches a critical connectivity. This transition, indicated by a suitably defined order parameter, is reflected by a sudden change of the network's topology when super-cycles are formed from merging isolated loops. After the oscillatory transition, the power spectra of simulated EEG time series exhibit a 1/f continuum superimposed with certain peaks. (c) 2007 Elsevier B.V. All rights reserved.
Original language | English |
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Pages (from-to) | 999-1007 |
Number of pages | 9 |
Journal | Neurocomputing |
Volume | 71 |
Issue number | 4-6 |
Early online date | 16 Mar 2007 |
DOIs | |
Publication status | Published - Jan 2008 |
Keywords
- EEG
- field potentials
- leaky integrator units
- random graphs
- phase transitions
- order parameter