Abstract
Modeling and interpreting (partial) synchronous neural activity can be a challenge. We illustrate this by deriving the phase dynamics of two seminal neural mass models: the Wilson-Cowan firing rate model and the voltage-based Freeman model. We established that the phase dynamics of these models differed qualitatively due to an attractive coupling in the first and a repulsive coupling in the latter. Using empirical structural connectivity matrices, we determined that the two dynamics cover the functional connectivity observed in resting state activity. We further searched for two pivotal dynamical features that have been reported in many experimental studies: (1) a partial phase synchrony with a possibility of a transition towards either a desynchronized or a (fully) synchronized state; (2) long-term autocorrelations indicative of a scale-free temporal dynamics of phase synchronization. Only the Freeman phase model exhibited scale-free behavior. Its repulsive coupling, however, let the individual phases disperse and did not allow for a transition into a synchronized state. The Wilson-Cowan phase model, by contrast, could switch into a (partially) synchronized state, but it did not generate long-term correlations although being located close to the onset of synchronization, i.e. in its critical regime. That is, the phase-reduced models can display one of the two dynamical features, but not both.
| Original language | English |
|---|---|
| Pages (from-to) | 428-441 |
| Number of pages | 14 |
| Journal | NeuroImage |
| Volume | 180 |
| Issue number | Part B |
| Early online date | 4 Apr 2018 |
| DOIs | |
| Publication status | Published - 15 Oct 2018 |
Keywords
- Criticality
- Freeman model
- Phase dynamics
- Power laws
- Synchronization
- Wilson-Cowan model
Cite this
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Scale-freeness or partial synchronization in neural mass phase oscillator networks : Pick one of two? / Daffertshofer, Andreas; Ton, Robert; Pietras, Bastian; Kringelbach, Morten L.; Deco, Gustavo.
In: NeuroImage, Vol. 180, No. Part B, 15.10.2018, p. 428-441.Research output: Contribution to Journal › Review article › Academic › peer-review
TY - JOUR
T1 - Scale-freeness or partial synchronization in neural mass phase oscillator networks
T2 - Pick one of two?
AU - Daffertshofer, Andreas
AU - Ton, Robert
AU - Pietras, Bastian
AU - Kringelbach, Morten L.
AU - Deco, Gustavo
PY - 2018/10/15
Y1 - 2018/10/15
N2 - Modeling and interpreting (partial) synchronous neural activity can be a challenge. We illustrate this by deriving the phase dynamics of two seminal neural mass models: the Wilson-Cowan firing rate model and the voltage-based Freeman model. We established that the phase dynamics of these models differed qualitatively due to an attractive coupling in the first and a repulsive coupling in the latter. Using empirical structural connectivity matrices, we determined that the two dynamics cover the functional connectivity observed in resting state activity. We further searched for two pivotal dynamical features that have been reported in many experimental studies: (1) a partial phase synchrony with a possibility of a transition towards either a desynchronized or a (fully) synchronized state; (2) long-term autocorrelations indicative of a scale-free temporal dynamics of phase synchronization. Only the Freeman phase model exhibited scale-free behavior. Its repulsive coupling, however, let the individual phases disperse and did not allow for a transition into a synchronized state. The Wilson-Cowan phase model, by contrast, could switch into a (partially) synchronized state, but it did not generate long-term correlations although being located close to the onset of synchronization, i.e. in its critical regime. That is, the phase-reduced models can display one of the two dynamical features, but not both.
AB - Modeling and interpreting (partial) synchronous neural activity can be a challenge. We illustrate this by deriving the phase dynamics of two seminal neural mass models: the Wilson-Cowan firing rate model and the voltage-based Freeman model. We established that the phase dynamics of these models differed qualitatively due to an attractive coupling in the first and a repulsive coupling in the latter. Using empirical structural connectivity matrices, we determined that the two dynamics cover the functional connectivity observed in resting state activity. We further searched for two pivotal dynamical features that have been reported in many experimental studies: (1) a partial phase synchrony with a possibility of a transition towards either a desynchronized or a (fully) synchronized state; (2) long-term autocorrelations indicative of a scale-free temporal dynamics of phase synchronization. Only the Freeman phase model exhibited scale-free behavior. Its repulsive coupling, however, let the individual phases disperse and did not allow for a transition into a synchronized state. The Wilson-Cowan phase model, by contrast, could switch into a (partially) synchronized state, but it did not generate long-term correlations although being located close to the onset of synchronization, i.e. in its critical regime. That is, the phase-reduced models can display one of the two dynamical features, but not both.
KW - Criticality
KW - Freeman model
KW - Phase dynamics
KW - Power laws
KW - Synchronization
KW - Wilson-Cowan model
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UR - http://www.scopus.com/inward/citedby.url?scp=85046167520&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2018.03.070
DO - 10.1016/j.neuroimage.2018.03.070
M3 - Review article
VL - 180
SP - 428
EP - 441
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
IS - Part B
ER -